392 Publikationen
2021 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2957373
Artelt A, Hinder F, Vaquet V, Feldhans R, Hammer B (2021)
Contrastive Explanations for Explaining Model Adaptations.
In: Advances in Computational Intelligence. 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Virtual Event, June 16–18, 2021, Proceedings, Part I. Rojas I, Joya G, Catala A (Eds); Lecture Notes in Computer Science, Cham: Springer International Publishing: 101-112.
PUB | DOI
Contrastive Explanations for Explaining Model Adaptations.
In: Advances in Computational Intelligence. 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Virtual Event, June 16–18, 2021, Proceedings, Part I. Rojas I, Joya G, Catala A (Eds); Lecture Notes in Computer Science, Cham: Springer International Publishing: 101-112.
2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2949334 
Rohlfing K, Cimiano P, Scharlau I, Matzner T, Buhl HM, Buschmeier H, Esposito E, Grimminger A, Hammer B, Häb-Umbach R, Horwath I, Hüllermeier E, Kern F, Kopp S, Thommes K, Ngonga Ngomo A-C, Schulte C, Wachsmuth H, Wagner P, Wrede B (2021)
Explanation as a social practice: Toward a conceptual framework for the social design of AI systems.
IEEE Transactions on Cognitive and Developmental Systems 13(3): 717--728.
PUB
| PDF | DOI
Explanation as a social practice: Toward a conceptual framework for the social design of AI systems.
IEEE Transactions on Cognitive and Developmental Systems 13(3): 717--728.
2021 | Zeitschriftenaufsatz | Im Druck | PUB-ID: 2957340
Artelt A, Hammer B (In Press)
Efficient computation of counterfactual explanations and counterfactual metrics of prototype-based classifiers.
Neurocomputing VSI: ESANN 2020.
PUB | DOI | Download (ext.)
Efficient computation of counterfactual explanations and counterfactual metrics of prototype-based classifiers.
Neurocomputing VSI: ESANN 2020.
2021 | Konferenzbeitrag | Angenommen | PUB-ID: 2956774
Hinder F, Hammer B (Accepted)
Concept Drift Segmentation via Kolmogorov Trees.
In: Proceedings of the ESANN, 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed);.
PUB
Concept Drift Segmentation via Kolmogorov Trees.
In: Proceedings of the ESANN, 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed);.
2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2956229
Paassen B, Schulz A, Stewart TC, Hammer B (2021)
Reservoir Memory Machines as Neural Computers.
IEEE Transactions on Neural Networks and Learning Systems: 1-11.
PUB | DOI | Download (ext.) | arXiv
Reservoir Memory Machines as Neural Computers.
IEEE Transactions on Neural Networks and Learning Systems: 1-11.
2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2954542
Paaßen B, Schulz A, Hammer B (2021)
Reservoir Stack Machines.
Neurocomputing.
PUB | DOI | Download (ext.) | arXiv
Reservoir Stack Machines.
Neurocomputing.
2021 | Konferenzbeitrag | Angenommen | PUB-ID: 2955948
Brinkrolf J, Hammer B (Accepted)
Federated Learning Vector Quantization.
In: Proceedings of the ESANN, 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed);.
PUB
Federated Learning Vector Quantization.
In: Proceedings of the ESANN, 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed);.
2021 | Zeitschriftenaufsatz | Angenommen | PUB-ID: 2955245
Stallmann D, Göpfert JP, Schmitz J, Grünberger A, Hammer B (Accepted)
Towards an automatic analysis of CHO-K1 suspension growth in microfluidic single-cell cultivation.
Bioinformatics .
PUB | DOI | PubMed | Europe PMC
Towards an automatic analysis of CHO-K1 suspension growth in microfluidic single-cell cultivation.
Bioinformatics .
2021 | Report | PUB-ID: 2954239
Szczuka J, Artelt A, Geminn C, Hammer B, Kopp S, Manzeschke A, Rossnagel A, Slawik P, Strathmann C, Szymczyk N, Varonina L, Weber C (2021)
Können Kinder aufgeklärte Nutzer* innen von Sprachassistenten sein? Rechtliche, psychologische, ethische und informatische Perspektiven.
Universität Duisburg-Essen, Universitätsbibliothek.
PUB | DOI
Können Kinder aufgeklärte Nutzer* innen von Sprachassistenten sein? Rechtliche, psychologische, ethische und informatische Perspektiven.
Universität Duisburg-Essen, Universitätsbibliothek.
2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2940666
Brinkrolf J, Hammer B (2020)
Sparse Metric Learning in Prototype-based Classification.
In: Proceedings of the ESANN, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); 375-380.
PUB
Sparse Metric Learning in Prototype-based Classification.
In: Proceedings of the ESANN, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); 375-380.
2020 | Konferenzbeitrag | PUB-ID: 2943260
Schulz A, Hinder F, Hammer B (2020)
DeepView: Visualizing Classification Boundaries of Deep Neural Networks as Scatter Plots Using Discriminative Dimensionality Reduction.
In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, {IJCAI-20}. .
PUB | DOI | Download (ext.)
DeepView: Visualizing Classification Boundaries of Deep Neural Networks as Scatter Plots Using Discriminative Dimensionality Reduction.
In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, {IJCAI-20}. .
2020 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2939517
Pfannschmidt L, Jakob J, Hinder F, Biehl M, Tino P, Hammer B (2020)
Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information.
Neurocomputing.
PUB | DOI | Download (ext.) | WoS | arXiv
Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information.
Neurocomputing.
2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2946761
Artelt A, Hammer B (2020)
Convex Density Constraints for Computing Plausible Counterfactual Explanations.
In: Artificial Neural Networks and Machine Learning - ICANN 2020 - 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15-18, 2020, Proceedings, Part {I}. Farkas I, Masulli P, Wermter S (Eds); Lecture Notes in Computer Science, 12396. Cham: Springer: 353-365.
PUB | DOI | Download (ext.)
Convex Density Constraints for Computing Plausible Counterfactual Explanations.
In: Artificial Neural Networks and Machine Learning - ICANN 2020 - 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15-18, 2020, Proceedings, Part {I}. Farkas I, Masulli P, Wermter S (Eds); Lecture Notes in Computer Science, 12396. Cham: Springer: 353-365.
2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2946685
Artelt A, Hammer B (2020)
Efficient computation of counterfactual explanations of LVQ models.
In: ESANN 2020 - proceedings. Verleysen M (Ed); Louvain-la-Neuve: Ciaco : 19-24.
PUB | Download (ext.)
Efficient computation of counterfactual explanations of LVQ models.
In: ESANN 2020 - proceedings. Verleysen M (Ed); Louvain-la-Neuve: Ciaco : 19-24.
2020 | Konferenzbeitrag | PUB-ID: 2946488
Hinder F, Artelt A, Hammer B (2020)
Towards non-parametric drift detection via Dynamic Adapting Window Independence Drift Detection (DAWIDD).
In: Proceedings of the 37th International Conference on Machine Learning. .
PUB | Download (ext.)
Towards non-parametric drift detection via Dynamic Adapting Window Independence Drift Detection (DAWIDD).
In: Proceedings of the 37th International Conference on Machine Learning. .
2019 | Monographie | PUB-ID: 2935200
Paaßen B, Artelt A, Hammer B (2019)
Lecture Notes on Applied Optimization.
Faculty of Technology, Bielefeld University.
PUB
| Dateien verfügbar
Lecture Notes on Applied Optimization.
Faculty of Technology, Bielefeld University.
2019 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2934458
Prahm C, Schulz A, Paaßen B, Schoisswohl J, Kaniusas E, Dorffner G, Hammer B, Aszmann O (2019)
Counteracting Electrode Shifts in Upper-Limb Prosthesis Control via Transfer Learning.
IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(5): 956-962.
PUB
| PDF | DOI | WoS | PubMed | Europe PMC
Counteracting Electrode Shifts in Upper-Limb Prosthesis Control via Transfer Learning.
IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(5): 956-962.
2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2933893
Pfannschmidt L, Jakob J, Biehl M, Tino P, Hammer B (2019)
Feature Relevance Bounds for Ordinal Regression.
In: Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019). Verleysen M (Ed); Louvain-la-Neuve: i6doc.
PUB | Download (ext.) | arXiv
Feature Relevance Bounds for Ordinal Regression.
In: Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019). Verleysen M (Ed); Louvain-la-Neuve: i6doc.
2019 | Konferenzbeitrag | Angenommen | PUB-ID: 2937841
Hosseini B, Hammer B (Accepted)
Interpretable Multiple-Kernel Prototype Learning for Discriminative Representation and Feature Selection.
Presented at the The 28th ACM International Conference on Information and Knowledge Management (CIKM) , Beijing.
PUB
| Datei | arXiv
Interpretable Multiple-Kernel Prototype Learning for Discriminative Representation and Feature Selection.
Presented at the The 28th ACM International Conference on Information and Knowledge Management (CIKM) , Beijing.
2019 | Report | Veröffentlicht | PUB-ID: 2937888
Krämer N, Artelt A, Geminn C, Hammer B, Kopp S, Manzeschke A, Rossnagel A, Slawik P, Szczuka J, Varonina L, Weber C (2019)
KI-basierte Sprachassistenten im Alltag: Forschungsbedarf aus informatischer, psychologischer, ethischer und rechtlicher Sicht.
Universität Duisburg-Essen, Universitätsbibliothek.
PUB | DOI | Download (ext.)
KI-basierte Sprachassistenten im Alltag: Forschungsbedarf aus informatischer, psychologischer, ethischer und rechtlicher Sicht.
Universität Duisburg-Essen, Universitätsbibliothek.
2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2937839
Hosseini B, Hammer B (2019)
Interpretable Discriminative Dimensionality Reduction and Feature Selection on the Manifold.
Presented at the 2019 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML), Würzburg.
PUB
| Datei | arXiv
Interpretable Discriminative Dimensionality Reduction and Feature Selection on the Manifold.
Presented at the 2019 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML), Würzburg.
2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2935456
Pfannschmidt L, Göpfert C, Neumann U, Heider D, Hammer B (2019)
FRI - Feature Relevance Intervals for Interpretable and Interactive Data Exploration.
Presented at the 16th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, Certosa di Pontignano, Siena - Tuscany, Italy.
PUB
| PDF | DOI | arXiv
FRI - Feature Relevance Intervals for Interpretable and Interactive Data Exploration.
Presented at the 16th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, Certosa di Pontignano, Siena - Tuscany, Italy.
2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2932412
Straat M, Abadi F, Göpfert C, Hammer B, Biehl M (2018)
Statistical Mechanics of On-Line Learning Under Concept Drift.
ENTROPY 20(10): 775.
PUB | DOI | WoS | PubMed | Europe PMC
Statistical Mechanics of On-Line Learning Under Concept Drift.
ENTROPY 20(10): 775.
2018 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2917896
Lux M, Brinkman RR, Chauve C, Laing A, Lorenc A, Abeler-Dörner L, Hammer B (2018)
flowLearn: Fast and precise identification and quality checking of cell populations in flow cytometry.
Bioinformatics 34(13): 2245-2253.
PUB | DOI | WoS | PubMed | Europe PMC
flowLearn: Fast and precise identification and quality checking of cell populations in flow cytometry.
Bioinformatics 34(13): 2245-2253.
2018 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2933557
Meyer S, Bertrand O, Egelhaaf M, Hammer B (2018)
Inferring Temporal Structure from Predictability in Bumblebee Learning Flight.
In: Intelligent Data Engineering and Automated Learning – IDEAL 2018. Yin H, Camacho D, Novais P, Tallón-Ballesteros AJ (Eds); Lecture Notes in Computer Science, 11314. Cham: Springer International Publishing: 508-519.
PUB | DOI
Inferring Temporal Structure from Predictability in Bumblebee Learning Flight.
In: Intelligent Data Engineering and Automated Learning – IDEAL 2018. Yin H, Camacho D, Novais P, Tallón-Ballesteros AJ (Eds); Lecture Notes in Computer Science, 11314. Cham: Springer International Publishing: 508-519.
2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2918254
Brinkrolf J, Berger K, Hammer B (2018)
Differential private relevance learning.
In: Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018). Verleysen M (Ed); 555-560.
PUB | Download (ext.)
Differential private relevance learning.
In: Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018). Verleysen M (Ed); 555-560.
2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2911900
Paaßen B, Göpfert C, Hammer B (2018)
Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces.
Neural Processing Letters 48(2): 669-689.
PUB | DOI | Download (ext.) | WoS | arXiv
Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces.
Neural Processing Letters 48(2): 669-689.
2018 | Preprint | Veröffentlicht | PUB-ID: 2921209
Hosseini B, Hammer B (2018)
Non-Negative Local Sparse Coding for Subspace Clustering.
Advances in Intelligent Data Analysis XVII. IDA 2018.
PUB
| Datei | Download (ext.) | arXiv
Non-Negative Local Sparse Coding for Subspace Clustering.
Advances in Intelligent Data Analysis XVII. IDA 2018.
2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2919598
Hosseini B, Hammer B (2018)
Feasibility Based Large Margin Nearest Neighbor Metric Learning.
In: ESANN 2018. Proceedings of 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 219-224.
PUB | arXiv
Feasibility Based Large Margin Nearest Neighbor Metric Learning.
In: ESANN 2018. Proceedings of 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 219-224.
2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2914505
Paaßen B, Schulz A, Hahne J, Hammer B (2018)
Expectation maximization transfer learning and its application for bionic hand prostheses.
Neurocomputing 298: 122-133.
PUB | DOI | Download (ext.) | WoS | arXiv
Expectation maximization transfer learning and its application for bionic hand prostheses.
Neurocomputing 298: 122-133.
2018 | Datenpublikation | PUB-ID: 2930611
Hülsmann F, Göpfert JP, Hammer B, Kopp S, Botsch M (2018)
Classification of motor errors to provide real-time feedback for sports coaching in virtual reality - A case study in squats and Tai Chi pushes (Data).
Bielefeld University.
PUB
| Dateien verfügbar | DOI
Classification of motor errors to provide real-time feedback for sports coaching in virtual reality - A case study in squats and Tai Chi pushes (Data).
Bielefeld University.
2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2921316
Göpfert JP, Hammer B, Wersing H (2018)
Mitigating Concept Drift via Rejection.
In: Artificial Neural Networks and Machine Learning – ICANN 2018. Proceedings, Part I. Kurkova V, Manolopoulos Y, Hammer B, Iliadis L, Maglogiannis I (Eds); Lecture Notes in Computer Science, 11139. Cham: Springer.
PUB
| PDF | DOI
Mitigating Concept Drift via Rejection.
In: Artificial Neural Networks and Machine Learning – ICANN 2018. Proceedings, Part I. Kurkova V, Manolopoulos Y, Hammer B, Iliadis L, Maglogiannis I (Eds); Lecture Notes in Computer Science, 11139. Cham: Springer.
2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2930862
Hülsmann F, Göpfert JP, Hammer B, Kopp S, Botsch M (2018)
Classification of motor errors to provide real-time feedback for sports coaching in virtual reality — A case study in squats and Tai Chi pushes.
Computers & Graphics 76: 47-59.
PUB | DOI | Download (ext.) | WoS
Classification of motor errors to provide real-time feedback for sports coaching in virtual reality — A case study in squats and Tai Chi pushes.
Computers & Graphics 76: 47-59.
2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2931283
Queißer J, Ishihara H, Hammer B, Steil JJ, Asada M (2018)
Skill Memories for Parameterized Dynamic Action Primitives on the Pneumatically Driven Humanoid Robot Child Affetto.
Presented at the International Conference on Development and Learning and on Epigenetic Robotics 2018 (ICDL-EPIROB2018), Tokyo .
PUB
| PDF
Skill Memories for Parameterized Dynamic Action Primitives on the Pneumatically Driven Humanoid Robot Child Affetto.
Presented at the International Conference on Development and Learning and on Epigenetic Robotics 2018 (ICDL-EPIROB2018), Tokyo .
2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2913389
Paaßen B, Hammer B, Price T, Barnes T, Gross S, Pinkwart N (2018)
The Continuous Hint Factory - Providing Hints in Vast and Sparsely Populated Edit Distance Spaces.
Journal of Educational Data Mining 10(1): 1-35.
PUB | Download (ext.) | arXiv
The Continuous Hint Factory - Providing Hints in Vast and Sparsely Populated Edit Distance Spaces.
Journal of Educational Data Mining 10(1): 1-35.
2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2919844
Paaßen B, Gallicchio C, Micheli A, Hammer B (2018)
Tree Edit Distance Learning via Adaptive Symbol Embeddings.
In: Proceedings of the 35th International Conference on Machine Learning (ICML 2018). Dy J, Krause A (Eds); Proceedings of Machine Learning Research, 80. 3973-3982.
PUB | Download (ext.) | arXiv
Tree Edit Distance Learning via Adaptive Symbol Embeddings.
In: Proceedings of the 35th International Conference on Machine Learning (ICML 2018). Dy J, Krause A (Eds); Proceedings of Machine Learning Research, 80. 3973-3982.
2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909369
Paaßen B, Schulz A, Hahne J, Hammer B (2017)
An EM transfer learning algorithm with applications in bionic hand prostheses.
In: Proceedings of the 25th European Symposium on Artificial Neural Networks (ESANN 2017). Verleysen M (Ed); Bruges: i6doc.com: 129-134.
PUB
| PDF
An EM transfer learning algorithm with applications in bionic hand prostheses.
In: Proceedings of the 25th European Symposium on Artificial Neural Networks (ESANN 2017). Verleysen M (Ed); Bruges: i6doc.com: 129-134.
2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2914945
Brinkrolf J, Hammer B (2017)
Probabilistic extension and reject options for pairwise LVQ.
In: 2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM). Piscataway, NJ: IEEE.
PUB | DOI
Probabilistic extension and reject options for pairwise LVQ.
In: 2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM). Piscataway, NJ: IEEE.
2017 | Konferenzbeitrag | PUB-ID: 2909371
Biehl M, Hammer B, Villmann T (2017)
Prototype based models for the supervised learning of classificaton schemes.
In: Proc. of the IAU Symposium 325 on Astroinformatics, Sorrento/Italy, October 2016. in press.
PUB
Prototype based models for the supervised learning of classificaton schemes.
In: Proc. of the IAU Symposium 325 on Astroinformatics, Sorrento/Italy, October 2016. in press.
2017 | Konferenzbeitrag | PUB-ID: 2914950
Brinkrolf J, Berger K, Hammer B (2017)
Differential Privacy for Learning Vector Quantization.
In: New Challenges in Neural Computation. .
PUB
Differential Privacy for Learning Vector Quantization.
In: New Challenges in Neural Computation. .
2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2908201
Göpfert C, Pfannschmidt L, Hammer B (2017)
Feature Relevance Bounds for Linear Classification.
In: Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Louvain-la-Neuve: Ciaco - i6doc.com: 187--192.
PUB
| Dateien verfügbar | Download (ext.)
Feature Relevance Bounds for Linear Classification.
In: Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Louvain-la-Neuve: Ciaco - i6doc.com: 187--192.
2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2913752
Göpfert JP, Göpfert C, Botsch M, Hammer B (2017)
Effects of Variability in Synthetic Training Data on Convolutional Neural Networks for 3D Head Reconstruction.
In: 2017 SSCI Proceedings. 2017 IEEE Symposium Series on Computational Intelligence (SSCI). Piscataway, NJ: IEEE.
PUB
| PDF | DOI
Effects of Variability in Synthetic Training Data on Convolutional Neural Networks for 3D Head Reconstruction.
In: 2017 SSCI Proceedings. 2017 IEEE Symposium Series on Computational Intelligence (SSCI). Piscataway, NJ: IEEE.
2017 | Konferenzbeitrag | PUB-ID: 2909370
Frenay B, Hammer B (2017)
Label-Noise-Tolerant Classification for Streaming Data.
In: IEEE International Joint Conference on Neural Neworks. .
PUB
Label-Noise-Tolerant Classification for Streaming Data.
In: IEEE International Joint Conference on Neural Neworks. .
2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2914141
Aswolinskiy W, Hammer B (2017)
Unsupervised Transfer Learning for Time Series via Self-Predictive Modelling - First Results.
In: Proceedings of the Workshop on New Challenges in Neural Computation (NC2). Machine Learning Reports, 03/2017. Bielefeld: Universität Bielefeld, CITEC.
PUB
| PDF
Unsupervised Transfer Learning for Time Series via Self-Predictive Modelling - First Results.
In: Proceedings of the Workshop on New Challenges in Neural Computation (NC2). Machine Learning Reports, 03/2017. Bielefeld: Universität Bielefeld, CITEC.
2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909037
Prahm C, Schulz A, Paaßen B, Aszmann O, Hammer B, Dorffner G (2017)
Echo State Networks as Novel Approach for Low-Cost Myoelectric Control.
In: Proceedings of the 16th Conference on Artificial Intelligence in Medicine (AIME 2017). ten Telje A, Popow C, Holmes JH, Sacchi L (Eds); Lecture Notes in Computer Science, 10259. Springer: 338--342.
PUB
| Dateien verfügbar | DOI
Echo State Networks as Novel Approach for Low-Cost Myoelectric Control.
In: Proceedings of the 16th Conference on Artificial Intelligence in Medicine (AIME 2017). ten Telje A, Popow C, Holmes JH, Sacchi L (Eds); Lecture Notes in Computer Science, 10259. Springer: 338--342.
2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2915274
Göpfert C, Göpfert JP, Hammer B (2017)
Analyzing Feature Relevance for Linear Reject Option SVM using Relevance Intervals.
In: Proceedings of the 2017 NIPS workshop on Transparent and Interpretable Machine Learning in Safety Critical Environments. .
PUB
| PDF
Analyzing Feature Relevance for Linear Reject Option SVM using Relevance Intervals.
In: Proceedings of the 2017 NIPS workshop on Transparent and Interpretable Machine Learning in Safety Critical Environments. .
2016 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2907633
Lux M, Krüger J, Rinke C, Maus I, Schlüter A, Woyke T, Sczyrba A, Hammer B (2016)
acdc – Automated Contamination Detection and Confidence estimation for single-cell genome data.
BMC Bioinformatics 17(1): 543.
PUB
| PDF | DOI | WoS | PubMed | Europe PMC
acdc – Automated Contamination Detection and Confidence estimation for single-cell genome data.
BMC Bioinformatics 17(1): 543.
2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909367
Kummert J, Paaßen B, Jensen J, Göpfert C, Hammer B (2016)
Local Reject Option for Deterministic Multi-class SVM.
In: Artificial Neural Networks and Machine Learning - ICANN 2016 - 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II. E.P. Villa A, Masulli P, Pons Rivero AJ (Eds); Lecture Notes in Computer Science, 9887. Cham: Springer Nature: 251--258.
PUB | DOI
Local Reject Option for Deterministic Multi-class SVM.
In: Artificial Neural Networks and Machine Learning - ICANN 2016 - 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II. E.P. Villa A, Masulli P, Pons Rivero AJ (Eds); Lecture Notes in Computer Science, 9887. Cham: Springer Nature: 251--258.
2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2904469
Hosseini B, Hülsmann F, Botsch M, Hammer B (2016)
Non-Negative Kernel Sparse Coding for the Analysis of Motion Data.
In: Artificial Neural Networks and Machine Learning – ICANN 2016. E.P. Villa A, Masulli P, Javier Pons Rivero A (Eds); Lecture Notes in Computer Science, 9887. Cham: Springer: 506-514.
PUB
| PDF | DOI | Download (ext.) | arXiv
Non-Negative Kernel Sparse Coding for the Analysis of Motion Data.
In: Artificial Neural Networks and Machine Learning – ICANN 2016. E.P. Villa A, Masulli P, Javier Pons Rivero A (Eds); Lecture Notes in Computer Science, 9887. Cham: Springer: 506-514.
2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900676
Paaßen B, Göpfert C, Hammer B (2016)
Gaussian process prediction for time series of structured data.
In: Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Louvain-la-Neuve: Ciaco - i6doc.com: 41--46.
PUB
| PDF
Gaussian process prediction for time series of structured data.
In: Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Louvain-la-Neuve: Ciaco - i6doc.com: 41--46.
2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2904509
Paaßen B, Jensen J, Hammer B (2016)
Execution Traces as a Powerful Data Representation for Intelligent Tutoring Systems for Programming.
In: Proceedings of the 9th International Conference on Educational Data Mining. Barnes T, Chi M, Feng M (Eds); Raleigh, North Carolina, USA: International Educational Datamining Society: 183-190.
PUB | Download (ext.)
Execution Traces as a Powerful Data Representation for Intelligent Tutoring Systems for Programming.
In: Proceedings of the 9th International Conference on Educational Data Mining. Barnes T, Chi M, Feng M (Eds); Raleigh, North Carolina, USA: International Educational Datamining Society: 183-190.
2016 | Konferenzbeitrag | E-Veröff. vor dem Druck | PUB-ID: 2904909
Schulz A, Hammer B (2016)
Discriminative Dimensionality Reduction in Kernel Space.
In: ESANN2016 Proceedings. 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges, Belgium,27-29 April 2016. i6doc.com.
PUB
| PDF
Discriminative Dimensionality Reduction in Kernel Space.
In: ESANN2016 Proceedings. 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges, Belgium,27-29 April 2016. i6doc.com.
2016 | Konferenzbeitrag | PUB-ID: 2909365
Brinkrolf J, Mittag T, Joppen R, Dr\ A, Pietsch K-H, Hammer B (2016)
Virtual optimisation for improved production planning.
In: New Challenges in Neural Computation. .
PUB
Virtual optimisation for improved production planning.
In: New Challenges in Neural Computation. .
2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2905729
Göpfert C, Paaßen B, Hammer B (2016)
Convergence of Multi-pass Large Margin Nearest Neighbor Metric Learning.
In: Artificial Neural Networks and Machine Learning – ICANN 2016: 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II. E.P. Villa A, Masulli P, Pons Rivero AJ (Eds); Lecture Notes in Computer Science, 9887. Cham: Springer Nature: 510-517.
PUB
| PDF | DOI
Convergence of Multi-pass Large Margin Nearest Neighbor Metric Learning.
In: Artificial Neural Networks and Machine Learning – ICANN 2016: 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II. E.P. Villa A, Masulli P, Pons Rivero AJ (Eds); Lecture Notes in Computer Science, 9887. Cham: Springer Nature: 510-517.
2016 | Konferenzbeitrag | PUB-ID: 2908455
Losing V, Hammer B, Wersing H (2016)
Dedicated Memory Models for Continual Learning in the Presence of Concept Drift.
Presented at the Continual Learning Workshop of the Thirtieth Annual Conference on Neural Information Processing Systems (NIPS), Barcelona.
PUB
| PDF
Dedicated Memory Models for Continual Learning in the Presence of Concept Drift.
Presented at the Continual Learning Workshop of the Thirtieth Annual Conference on Neural Information Processing Systems (NIPS), Barcelona.
2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2905855
Paaßen B, Schulz A, Hammer B (2016)
Linear Supervised Transfer Learning for Generalized Matrix LVQ.
In: Proceedings of the Workshop New Challenges in Neural Computation 2016. Hammer B, Martinetz T, Villmann T (Eds); Machine Learning Reports, 11-18.
PUB | Download (ext.)
Linear Supervised Transfer Learning for Generalized Matrix LVQ.
In: Proceedings of the Workshop New Challenges in Neural Computation 2016. Hammer B, Martinetz T, Villmann T (Eds); Machine Learning Reports, 11-18.
2016 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2903457
Schleif F-M, Hammer B, Gonzalez Monroy J, Gonzalez Jimenez J, Blanco-Claraco J-L, Biehl M, Petkov N (2016)
Odor recognition in robotics applications by discriminative time-series modeling.
PATTERN ANALYSIS AND APPLICATIONS 19(1): 207-220.
PUB | DOI | WoS
Odor recognition in robotics applications by discriminative time-series modeling.
PATTERN ANALYSIS AND APPLICATIONS 19(1): 207-220.
2016 | Konferenzbeitrag | PUB-ID: 2909368
Geppert er, Hammer B (2016)
Incremental learning algorithms and applications.
In: ESANN. .
PUB
Incremental learning algorithms and applications.
In: ESANN. .
2016 | Konferenzbeitrag | PUB-ID: 2905195
Fischer L, Hammer B, Wersing H (2016)
Online Metric Learning for an Adaptation to Confidence Drift.
In: Proceedings of International Joint Conference on Neural Networks (IJCNN). Vancouver: IEEE: 748-755.
PUB
Online Metric Learning for an Adaptation to Confidence Drift.
In: Proceedings of International Joint Conference on Neural Networks (IJCNN). Vancouver: IEEE: 748-755.
2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2904178
Prahm C, Paaßen B, Schulz A, Hammer B, Aszmann O (2016)
Transfer Learning for Rapid Re-calibration of a Myoelectric Prosthesis after Electrode Shift.
In: Converging Clinical and Engineering Research on Neurorehabilitation II: Proceedings of the 3rd International Conference on NeuroRehabilitation (ICNR2016). Ibáñez J, Gonzáles-Vargas J, Azorín JM, Akay M, Pons JL (Eds); Springer: 153--157.
PUB
| PDF | DOI | Download (ext.)
Transfer Learning for Rapid Re-calibration of a Myoelectric Prosthesis after Electrode Shift.
In: Converging Clinical and Engineering Research on Neurorehabilitation II: Proceedings of the 3rd International Conference on NeuroRehabilitation (ICNR2016). Ibáñez J, Gonzáles-Vargas J, Azorín JM, Akay M, Pons JL (Eds); Springer: 153--157.
2016 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2910957
Biehl M, Hammer B, Villmann T (2016)
Prototype-based models in machine learning.
Wiley Interdisciplinary Reviews: Cognitive Science 7(2): 92-111.
PUB | DOI | WoS | PubMed | Europe PMC
Prototype-based models in machine learning.
Wiley Interdisciplinary Reviews: Cognitive Science 7(2): 92-111.
2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909366
Villmann T, Kaden M, Bohnsack A, Villmann JM, Drogies T, Saralajew S, Hammer B (2016)
Self-Adjusting Reject Options in Prototype Based Classification.
In: Advances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 11th International Workshop WSOM 2016, Houston, Texas, USA, January 6-8, 2016. Merényi E, Mendenhall MJ, O'Driscoll P (Eds); Advances in Intelligent Systems and Computing, 428. Cham: Springer International Publishing: 269-279.
PUB | DOI
Self-Adjusting Reject Options in Prototype Based Classification.
In: Advances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 11th International Workshop WSOM 2016, Houston, Texas, USA, January 6-8, 2016. Merényi E, Mendenhall MJ, O'Driscoll P (Eds); Advances in Intelligent Systems and Computing, 428. Cham: Springer International Publishing: 269-279.
2015 | Preprint | Veröffentlicht | PUB-ID: 2901613
Lux M, Hammer B, Sczyrba A (2015)
Automated Contamination Detection in Single-Cell Sequencing.
bioRxiv.
PUB
Automated Contamination Detection in Single-Cell Sequencing.
bioRxiv.
2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2783165
Hosseini B, Hammer B (2015)
Efficient Metric Learning for the Analysis of Motion Data.
In: 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA). Piscataway, NJ: IEEE.
PUB | DOI | Download (ext.) | arXiv
Efficient Metric Learning for the Analysis of Motion Data.
In: 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA). Piscataway, NJ: IEEE.
2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2724156
Paaßen B, Mokbel B, Hammer B (2015)
Adaptive structure metrics for automated feedback provision in Java programming.
In: Proceedings of the ESANN, 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); 307-312.
PUB
| PDF
Adaptive structure metrics for automated feedback provision in Java programming.
In: Proceedings of the ESANN, 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); 307-312.
2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2710031
Mokbel B, Paaßen B, Schleif F-M, Hammer B (2015)
Metric learning for sequences in relational LVQ.
Neurocomputing 169(SI): 306-322.
PUB
| PDF | DOI | Download (ext.) | WoS
Metric learning for sequences in relational LVQ.
Neurocomputing 169(SI): 306-322.
2015 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2900303
Schulz A, Hammer B (2015)
Visualization of Regression Models Using Discriminative Dimensionality Reduction.
In: Computer Analysis of Images and Patterns. Lecture Notes in Computer Science, 9257. Cham: Springer Science + Business Media: 437-449.
PUB
| PDF | DOI
Visualization of Regression Models Using Discriminative Dimensionality Reduction.
In: Computer Analysis of Images and Patterns. Lecture Notes in Computer Science, 9257. Cham: Springer Science + Business Media: 437-449.
2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900325
Blöbaum P, Schulz A, Hammer B (2015)
Unsupervised Dimensionality Reduction for Transfer Learning.
In: Proceedings. 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Louvain-la-Neuve: Ciaco: 507-512.
PUB
| PDF
Unsupervised Dimensionality Reduction for Transfer Learning.
In: Proceedings. 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Louvain-la-Neuve: Ciaco: 507-512.
2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900319
Schulz A, Hammer B (2015)
Discriminative dimensionality reduction for regression problems using the Fisher metric.
In: 2015 International Joint Conference on Neural Networks (IJCNN). Institute of Electrical & Electronics Engineers (IEEE): 1-8.
PUB | DOI
Discriminative dimensionality reduction for regression problems using the Fisher metric.
In: 2015 International Joint Conference on Neural Networks (IJCNN). Institute of Electrical & Electronics Engineers (IEEE): 1-8.
2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2774707
Fischer L, Hammer B, Wersing H (2015)
Certainty-based Prototype Insertion/Deletion for Classification with Metric Adaptation.
In: ESANN, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 7-12.
PUB
Certainty-based Prototype Insertion/Deletion for Classification with Metric Adaptation.
In: ESANN, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 7-12.
2015 | Konferenzbeitrag | PUB-ID: 2774721
Fischer L, Hammer B, Wersing H (2015)
Combining Offline and Online Classifiers for Life-long Learning.
In: IJCNN, International Joint Conference on Neural Networks. 2808-2815.
PUB
Combining Offline and Online Classifiers for Life-long Learning.
In: IJCNN, International Joint Conference on Neural Networks. 2808-2815.
2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2762087
Paaßen B, Mokbel B, Hammer B (2015)
A Toolbox for Adaptive Sequence Dissimilarity Measures for Intelligent Tutoring Systems.
In: Proceedings of the 8th International Conference on Educational Data Mining. Santos OC, Boticario JG, Romero C, Pechenizkiy M, Merceron A, Mitros P, Luna JM, Mihaescu C, Moreno P, Hershkovitz A, Ventura S, Desmarais M (Eds); International Educational Datamining Society: 632-632.
PUB | Download (ext.)
A Toolbox for Adaptive Sequence Dissimilarity Measures for Intelligent Tutoring Systems.
In: Proceedings of the 8th International Conference on Educational Data Mining. Santos OC, Boticario JG, Romero C, Pechenizkiy M, Merceron A, Mitros P, Luna JM, Mihaescu C, Moreno P, Hershkovitz A, Ventura S, Desmarais M (Eds); International Educational Datamining Society: 632-632.
2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2752948
Gross S, Mokbel B, Hammer B, Pinkwart N (2015)
Learning Feedback in Intelligent Tutoring Systems.
KI - Künstliche Intelligenz 29(4): 1-6.
PUB
| PDF | DOI | Download (ext.) | WoS
Learning Feedback in Intelligent Tutoring Systems.
KI - Künstliche Intelligenz 29(4): 1-6.
2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2752955
Walter O, Häb-Umbach R, Mokbel B, Paaßen B, Hammer B (2015)
Autonomous Learning of Representations.
KI - Künstliche Intelligenz 29(4): 339–351.
PUB
| PDF | DOI | Download (ext.) | WoS
Autonomous Learning of Representations.
KI - Künstliche Intelligenz 29(4): 339–351.
2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900320
Frenay B, Hofmann D, Schulz A, Biehl M, Hammer B (2014)
Valid interpretation of feature relevance for linear data mappings.
In: 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM). Institute of Electrical & Electronics Engineers (IEEE): 149-156.
PUB
| PDF | DOI
Valid interpretation of feature relevance for linear data mappings.
In: 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM). Institute of Electrical & Electronics Engineers (IEEE): 149-156.
2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2678214
Hofmann D, Schleif F-M, Paaßen B, Hammer B (2014)
Learning interpretable kernelized prototype-based models.
Neurocomputing 141: 84-96.
PUB | DOI | Download (ext.) | WoS
Learning interpretable kernelized prototype-based models.
Neurocomputing 141: 84-96.
2014 | Konferenzbeitrag | PUB-ID: 2909360
Gross S, Mokbel B, Hammer B, Pinkwart N (2014)
How to Select an Example? A Comparison of Selection Strategies in Example-Based Learning.
In: Intelligent Tutoring Systems. Trausan-Matu S, Elizabeth Boyer K, E. Crosby M, Panourgia K (Eds); Lecture Notes in Computer Science, 8474. Springer: 340-347.
PUB
How to Select an Example? A Comparison of Selection Strategies in Example-Based Learning.
In: Intelligent Tutoring Systems. Trausan-Matu S, Elizabeth Boyer K, E. Crosby M, Panourgia K (Eds); Lecture Notes in Computer Science, 8474. Springer: 340-347.
2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2774643
Fischer L, Nebel D, Villmann T, Hammer B, Wersing H (2014)
Rejection Strategies for Learning Vector Quantization – A Comparison of Probabilistic and Deterministic Approaches.
In: Advances in Self-Organizing Maps and Learning Vector Quantization. Villmann T, Schleif F-M, Kaden M, Lange M (Eds); Advances in Intelligent Systems and Computing, 295. Cham: Springer International Publishing: 109-118.
PUB | DOI
Rejection Strategies for Learning Vector Quantization – A Comparison of Probabilistic and Deterministic Approaches.
In: Advances in Self-Organizing Maps and Learning Vector Quantization. Villmann T, Schleif F-M, Kaden M, Lange M (Eds); Advances in Intelligent Systems and Computing, 295. Cham: Springer International Publishing: 109-118.
2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2673548
Fischer L, Hammer B, Wersing H (2014)
Rejection strategies for learning vector quantization.
In: ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Bruges, Belgium: i6doc.com: 41-46.
PUB
Rejection strategies for learning vector quantization.
In: ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Bruges, Belgium: i6doc.com: 41-46.
2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2774498
Fischer L, Hammer B, Wersing H (2014)
Local Rejection Strategies for Learning Vector Quantization.
In: Artificial Neural Networks and Machine Learning – ICANN 2014. Wermter S, Weber C, Duch W, Honkela T, Koprinkova-Hristova P, Magg S, Palm G, Villa AEP (Eds); Lecture Notes in Computer Science, 8681. Cham: Springer International Publishing: 563-570.
PUB | DOI
Local Rejection Strategies for Learning Vector Quantization.
In: Artificial Neural Networks and Machine Learning – ICANN 2014. Wermter S, Weber C, Duch W, Honkela T, Koprinkova-Hristova P, Magg S, Palm G, Villa AEP (Eds); Lecture Notes in Computer Science, 8681. Cham: Springer International Publishing: 563-570.
2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2673554
Mokbel B, Paaßen B, Hammer B (2014)
Adaptive distance measures for sequential data.
In: ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Bruges, Belgium: i6doc.com: 265-270.
PUB
| PDF
Adaptive distance measures for sequential data.
In: ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Bruges, Belgium: i6doc.com: 265-270.
2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2673559
Hammer B, He H, Martinetz T (2014)
Learning and modeling big data.
In: ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Bruges, Belgium: i6doc.com: 343-352.
PUB
Learning and modeling big data.
In: ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Bruges, Belgium: i6doc.com: 343-352.
2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2734058
Gross S, Mokbel B, Paaßen B, Hammer B, Pinkwart N (2014)
Example-based feedback provision using structured solution spaces.
International Journal of Learning Technology 9(3): 248-280.
PUB | DOI | Download (ext.)
Example-based feedback provision using structured solution spaces.
International Journal of Learning Technology 9(3): 248-280.
2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2710067
Mokbel B, Paaßen B, Hammer B (2014)
Efficient Adaptation of Structure Metrics in Prototype-Based Classification.
In: Artificial Neural Networks and Machine Learning - ICANN 2014 - 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings. Wermter S, Weber C, Duch W, Honkela T, Koprinkova-Hristova P, Magg S, Palm G, Villa A (Eds); Lecture Notes in Computer Science, 8681. Springer: 571-578.
PUB
| PDF | DOI | Download (ext.)
Efficient Adaptation of Structure Metrics in Prototype-Based Classification.
In: Artificial Neural Networks and Machine Learning - ICANN 2014 - 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings. Wermter S, Weber C, Duch W, Honkela T, Koprinkova-Hristova P, Magg S, Palm G, Villa A (Eds); Lecture Notes in Computer Science, 8681. Springer: 571-578.
2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2673545
Nebel D, Hammer B, Villmann T (2014)
Supervised Generative Models for Learning Dissimilarity Data.
In: ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Bruges, Belgium: i6doc.com: 35-40.
PUB
Supervised Generative Models for Learning Dissimilarity Data.
In: ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Bruges, Belgium: i6doc.com: 35-40.
2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2673557
Schulz A, Gisbrecht A, Hammer B (2014)
Relevance learning for dimensionality reduction.
In: ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Bruges, Belgium: i6doc.com: 165-170.
PUB
Relevance learning for dimensionality reduction.
In: ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Bruges, Belgium: i6doc.com: 165-170.
2014 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2900324
Gisbrecht A, Schulz A, Hammer B (2014)
Discriminative Dimensionality Reduction for the Visualization of Classifiers.
In: Pattern Recognition Applications and Methods. Advances in Intelligent Systems and Computing, 318. Cham: Springer Science + Business Media: 39-56.
PUB | DOI
Discriminative Dimensionality Reduction for the Visualization of Classifiers.
In: Pattern Recognition Applications and Methods. Advances in Intelligent Systems and Computing, 318. Cham: Springer Science + Business Media: 39-56.
2014 | Konferenzbeitrag | PUB-ID: 2909361
Hammer B, Nebel D, Riedel M, Villmann T (2014)
Generative versus Discriminative Prototype Based Classification.
In: Advances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 10th International Workshop, {WSOM} 2014, Mittweida, Germany, July, 2-4, 2014. Cham: Springer International Publishing: 123--132.
PUB | DOI
Generative versus Discriminative Prototype Based Classification.
In: Advances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 10th International Workshop, {WSOM} 2014, Mittweida, Germany, July, 2-4, 2014. Cham: Springer International Publishing: 123--132.
2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2623500
Gisbrecht A, Hammer B, Mokbel B, Sczyrba A (2013)
Nonlinear dimensionality reduction for cluster identification in metagenomic samples.
In: 17th International Conference on Information Visualisation IV 2013. Banissi E (Ed); Piscataway, NJ: IEEE: 174-179.
PUB | DOI
Nonlinear dimensionality reduction for cluster identification in metagenomic samples.
In: 17th International Conference on Information Visualisation IV 2013. Banissi E (Ed); Piscataway, NJ: IEEE: 174-179.
2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625185
Mokbel B, Gross S, Paaßen B, Pinkwart N, Hammer B (2013)
Domain-Independent Proximity Measures in Intelligent Tutoring Systems.
In: Proceedings of the 6th International Conference on Educational Data Mining (EDM). D'Mello SK, Calvo RA, Olney A (Eds); 334-335.
PUB | Download (ext.)
Domain-Independent Proximity Measures in Intelligent Tutoring Systems.
In: Proceedings of the 6th International Conference on Educational Data Mining (EDM). D'Mello SK, Calvo RA, Olney A (Eds); 334-335.
2013 | Konferenzbeitrag | PUB-ID: 2909358
Strickert M, Hammer B, Villmann T, Biehl M (2013)
Regularization and Improved Interpretation of Linear Data Mappings and Adaptive Distance Measures.
In: IEEE SSCI CIDM 2013. IEEE Computational Intelligence Society: 10-17.
PUB
Regularization and Improved Interpretation of Linear Data Mappings and Adaptive Distance Measures.
In: IEEE SSCI CIDM 2013. IEEE Computational Intelligence Society: 10-17.
2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622456
Schulz A, Gisbrecht A, Hammer B (2013)
Using Nonlinear Dimensionality Reduction to Visualize Classifiers.
In: Advances in computational intelligence. Proceedings. Vol 1. Rojas I, Joya G, Gabestany J (Eds); Lecture Notes in Computer Science, 7902. Springer: 59-68.
PUB | DOI | WoS
Using Nonlinear Dimensionality Reduction to Visualize Classifiers.
In: Advances in computational intelligence. Proceedings. Vol 1. Rojas I, Joya G, Gabestany J (Eds); Lecture Notes in Computer Science, 7902. Springer: 59-68.
2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622467
Schulz A, Gisbrecht A, Hammer B (2013)
Classifier inspection based on different discriminative dimensionality reductions.
In: Workshop NC^2 2013. TR Machine Learning Reports: 77-86.
PUB
Classifier inspection based on different discriminative dimensionality reductions.
In: Workshop NC^2 2013. TR Machine Learning Reports: 77-86.
2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625194
Gisbrecht A, Miche Y, Hammer B, Lendasse A (2013)
Visualizing Dependencies of Spectral Features using Mutual Information.
In: ESANN, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 573-578.
PUB
Visualizing Dependencies of Spectral Features using Mutual Information.
In: ESANN, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 573-578.
2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625199
Hofmann D, Hammer B (2013)
Sparse approximations for kernel learning vector quantization.
In: ESANN. .
PUB
Sparse approximations for kernel learning vector quantization.
In: ESANN. .
2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625202
Schleif F-M, Zhu X, Hammer B (2013)
Sparse prototype representation by core sets.
In: IDEAL 2013. Hujun Yin et.al (Ed);.
PUB
Sparse prototype representation by core sets.
In: IDEAL 2013. Hujun Yin et.al (Ed);.
2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625207
Gross S, Mokbel B, Hammer B, Pinkwart N (2013)
Towards Providing Feedback to Students in Absence of Formalized Domain Models.
In: AIED. 644-648.
PUB
Towards Providing Feedback to Students in Absence of Formalized Domain Models.
In: AIED. 644-648.
2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615701
Zhu X, Schleif F-M, Hammer B (2013)
Semi-Supervised Vector Quantization for proximity data.
In: Proceedings of ESANN 2013. 89-94.
PUB
Semi-Supervised Vector Quantization for proximity data.
In: Proceedings of ESANN 2013. 89-94.
2013 | Konferenzbeitrag | PUB-ID: 2909359
Nebel D, Hammer B, Villmann T (2013)
A Median Variant of Generalized Learning Vector Quantization.
In: ICONIP (2). 19-26.
PUB
A Median Variant of Generalized Learning Vector Quantization.
In: ICONIP (2). 19-26.
2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2625232
Gisbrecht A, Mokbel B, Schleif F-M, Zhu X, Hammer B (2012)
Linear Time Relational Prototype Based Learning.
Int. J. Neural Syst. 22(5): 1250021.
PUB | DOI | WoS | PubMed | Europe PMC
Linear Time Relational Prototype Based Learning.
Int. J. Neural Syst. 22(5): 1250021.
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622449
Schulz A, Gisbrecht A, Bunte K, Hammer B (2012)
How to visualize a classifier?
In: Proceedings of the Workshop - New Challenges in Neural Computation 2012. Machine Learning Reports: 73-83.
PUB
How to visualize a classifier?
In: Proceedings of the Workshop - New Challenges in Neural Computation 2012. Machine Learning Reports: 73-83.
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625260
Gisbrecht A, Lueks W, Mokbel B, Hammer B (2012)
Out-of-sample kernel extensions for nonparametric dimensionality reduction.
In: ESANN 2012. 531-536.
PUB
Out-of-sample kernel extensions for nonparametric dimensionality reduction.
In: ESANN 2012. 531-536.
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625265
Gisbrecht A, Sovilj D, Hammer B, Lendasse A (2012)
Relevance learning for time series inspection.
In: ESANN 2012. Verleysen M (Ed); 489-494.
PUB
Relevance learning for time series inspection.
In: ESANN 2012. Verleysen M (Ed); 489-494.
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2671172
Hofmann D, Gisbrecht A, Hammer B (2012)
Discriminative probabilistic prototype based models in kernel space.
In: Workshop NC^2 2012. TR Machine Learning Reports.
PUB
Discriminative probabilistic prototype based models in kernel space.
In: Workshop NC^2 2012. TR Machine Learning Reports.
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2536426
Mokbel B, Gross S, Lux M, Pinkwart N, Hammer B (2012)
How to Quantitatively Compare Data Dissimilarities for Unsupervised Machine Learning?
In: Artificial Neural Networks in Pattern Recognition. 5th INNS IAPR TC 3 GIRPR Workshop, ANNPR 2012. Proceedings. Mana N, Schwenker F, Trentin E (Eds); Lecture Notes in Artificial Intelligence, 7477. Springer Berlin Heidelberg: 1-13.
PUB
| PDF | DOI
How to Quantitatively Compare Data Dissimilarities for Unsupervised Machine Learning?
In: Artificial Neural Networks in Pattern Recognition. 5th INNS IAPR TC 3 GIRPR Workshop, ANNPR 2012. Proceedings. Mana N, Schwenker F, Trentin E (Eds); Lecture Notes in Artificial Intelligence, 7477. Springer Berlin Heidelberg: 1-13.
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625238
Hofmann D, Gisbrecht A, Hammer B (2012)
Efficient Approximations of Kernel Robust Soft LVQ.
In: WSOM. .
PUB
Efficient Approximations of Kernel Robust Soft LVQ.
In: WSOM. .
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625271
Bouveyron C, Hammer B, Villmann T (2012)
Recent developments in clustering algorithms.
In: ESANN 2012. Verleysen M (Ed); 447-458.
PUB
Recent developments in clustering algorithms.
In: ESANN 2012. Verleysen M (Ed); 447-458.
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625276
Gisbrecht A, Mokbel B, Hammer B (2012)
Linear Basis-Function t-SNE for Fast Nonlinear Dimensionality Reduction.
In: IJCNN. .
PUB
Linear Basis-Function t-SNE for Fast Nonlinear Dimensionality Reduction.
In: IJCNN. .
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625242
Gross S, Mokbel B, Hammer B, Pinkwart N (2012)
Feedback Provision Strategies in Intelligent Tutoring Systems Based on Clustered Solution Spaces.
In: DeLFI. 27-38.
PUB
Feedback Provision Strategies in Intelligent Tutoring Systems Based on Clustered Solution Spaces.
In: DeLFI. 27-38.
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625247
Gisbrecht A, Hofmann D, Hammer B (2012)
Discriminative Dimensionality Reduction Mappings.
In: Advances in Intelligent Data Analysis XI - 11th International Symposium, IDA 2012, Helsinki, Finland, October 25-27, 2012. Proceedings. Hollmén J, Klawonn F, Tucker A (Eds); Lecture Notes in Computer Science, 7619. Springer: 126-138.
PUB
Discriminative Dimensionality Reduction Mappings.
In: Advances in Intelligent Data Analysis XI - 11th International Symposium, IDA 2012, Helsinki, Finland, October 25-27, 2012. Proceedings. Hollmén J, Klawonn F, Tucker A (Eds); Lecture Notes in Computer Science, 7619. Springer: 126-138.
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625254
Hofmann D, Hammer B (2012)
Kernel Robust Soft Learning Vector Quantization.
In: Artificial Neural Networks in Pattern Recognition - 5th INNS IAPR TC 3 GIRPR Workshop, ANNPR 2012, Trento, Italy, September 17-19, 2012. Proceedings. Mana N, Schwenker F, Trentin E (Eds); Lecture Notes in Computer Science, 7477. Springer: 14-23.
PUB
Kernel Robust Soft Learning Vector Quantization.
In: Artificial Neural Networks in Pattern Recognition - 5th INNS IAPR TC 3 GIRPR Workshop, ANNPR 2012, Trento, Italy, September 17-19, 2012. Proceedings. Mana N, Schwenker F, Trentin E (Eds); Lecture Notes in Computer Science, 7477. Springer: 14-23.
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615750
Schleif F-M, Zhu X, Gisbrecht A, Hammer B (2012)
Fast approximated relational and kernel clustering.
In: Proceedings of ICPR 2012. IEEE: 1229-1232.
PUB
Fast approximated relational and kernel clustering.
In: Proceedings of ICPR 2012. IEEE: 1229-1232.
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2536437
Gross S, Zhu X, Hammer B, Pinkwart N (2012)
Cluster based feedback provision strategies in intelligent tutoring systems.
In: Proceedings of the 11th international conference on Intelligent Tutoring Systems. Berlin, Heidelberg: Springer-Verlag: 699-700.
PUB
| PDF | DOI | Download (ext.)
Cluster based feedback provision strategies in intelligent tutoring systems.
In: Proceedings of the 11th international conference on Intelligent Tutoring Systems. Berlin, Heidelberg: Springer-Verlag: 699-700.
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2536444
Gross S, Mokbel B, Hammer B, Pinkwart N (2012)
Feedback Provision Strategies in Intelligent Tutoring Systems Based on Clustered Solution Spaces.
In: DeLFI 2012: Die 10. e-Learning Fachtagung Informatik. Desel J, Haake JM, Spannagel C, Gesellschaft für Informatik (Eds); GI-Edition : Proceedings, 207. Hagen, Germany: Köllen: 27-38.
PUB
| PDF
Feedback Provision Strategies in Intelligent Tutoring Systems Based on Clustered Solution Spaces.
In: DeLFI 2012: Die 10. e-Learning Fachtagung Informatik. Desel J, Haake JM, Spannagel C, Gesellschaft für Informatik (Eds); GI-Edition : Proceedings, 207. Hagen, Germany: Köllen: 27-38.
2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2534839
Gisbrecht A, Mokbel B, Schleif F-M, Zhu X, Hammer B (2012)
Linear Time Relational Prototype Based Learning.
Int. J. Neural Syst. 22(05): 1250021.
PUB | DOI | WoS | PubMed | Europe PMC
Linear Time Relational Prototype Based Learning.
Int. J. Neural Syst. 22(05): 1250021.
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534877
Schleif F-M, Mokbel B, Gisbrecht A, Theunissen L, Dürr V, Hammer B (2012)
Learning Relevant Time Points for Time-Series Data in the Life Sciences.
In: ICANN (2). Lecture Notes in Computer Science, 7553. Berlin, Heidelberg: Springer Berlin Heidelberg: 531-539.
PUB | DOI
Learning Relevant Time Points for Time-Series Data in the Life Sciences.
In: ICANN (2). Lecture Notes in Computer Science, 7553. Berlin, Heidelberg: Springer Berlin Heidelberg: 531-539.
2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2489405
Bunte K, Schneider P, Hammer B, Schleif F-M, Villmann T, Biehl M (2012)
Limited Rank Matrix Learning, discriminative dimension reduction and visualization.
Neural Networks 26: 159-173.
PUB | DOI | WoS | PubMed | Europe PMC
Limited Rank Matrix Learning, discriminative dimension reduction and visualization.
Neural Networks 26: 159-173.
2012 | Konferenzbeitrag | PUB-ID: 2909356
Mokbel B, Lueks W, Gisbrecht A, Biehl M, Hammer B (2012)
Visualizing the quality of dimensionality reduction.
In: ESANN 2012. Verleysen M (Ed); 179--184.
PUB
Visualizing the quality of dimensionality reduction.
In: ESANN 2012. Verleysen M (Ed); 179--184.
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534905
Schleif F-M, Gisbrecht A, Hammer B (2012)
Relevance learning for short high-dimensional time series in the life sciences.
In: IJCNN. IEEE Computational Intelligence Society, Institute of Electrical and Electronics Engineers (Eds); Piscataway, NJ: IEEE: 1-8.
PUB | DOI
Relevance learning for short high-dimensional time series in the life sciences.
In: IJCNN. IEEE Computational Intelligence Society, Institute of Electrical and Electronics Engineers (Eds); Piscataway, NJ: IEEE: 1-8.
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276480
Gisbrecht A, Schleif F-M, Zhu X, Hammer B (2011)
Linear time heuristics for topographic mapping of dissimilarity data.
In: Intelligent Data Engineering and Automated Learning - IDEAL 2011: IDEAL 2011, 12th international conference, Norwich, UK, September 7 - 9, 2011 ; proceedings. Lecture Notes in Computer Science, 6936. Berlin, Heidelberg: Springer: 25-33.
PUB | DOI
Linear time heuristics for topographic mapping of dissimilarity data.
In: Intelligent Data Engineering and Automated Learning - IDEAL 2011: IDEAL 2011, 12th international conference, Norwich, UK, September 7 - 9, 2011 ; proceedings. Lecture Notes in Computer Science, 6936. Berlin, Heidelberg: Springer: 25-33.
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276485
Hammer B, Gisbrecht A, Hasenfuss A, Mokbel B, Schleif F-M, Zhu X (2011)
Topographic Mapping of Dissimilarity Data.
In: WSOM'11. .
PUB
Topographic Mapping of Dissimilarity Data.
In: WSOM'11. .
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276492
Schleif F-M, Gisbrecht A, Hammer B (2011)
Accelerating Kernel Neural Gas.
In: ICANN'2011. Kaski S, Honkela T, Gitolami M, Dutch W (Eds);.
PUB
Accelerating Kernel Neural Gas.
In: ICANN'2011. Kaski S, Honkela T, Gitolami M, Dutch W (Eds);.
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276500
Kaestner M, Hammer B, Biehl M, Villmann T (2011)
Generalized Functional Relevance Learning Vector Quantization.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); D side: pp. 93-98.
PUB
Generalized Functional Relevance Learning Vector Quantization.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); D side: pp. 93-98.
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276512
Hammer B, Biehl M, Bunte K, Mokbel B (2011)
A general framework for dimensionality reduction for large data sets.
In: WSOM'11. .
PUB
A general framework for dimensionality reduction for large data sets.
In: WSOM'11. .
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276517
Bunte K, Biehl M, Hammer B (2011)
Supervised dimension reduction mappings.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); D side: pp. 281-286.
PUB
Supervised dimension reduction mappings.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); D side: pp. 281-286.
2011 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2309980
Schleif F-M, Villmann T, Hammer B, Schneider P (2011)
Efficient Kernelized Prototype-based Classification.
International Journal of Neural Systems 21(06): 443-457.
PUB | DOI | WoS | PubMed | Europe PMC
Efficient Kernelized Prototype-based Classification.
International Journal of Neural Systems 21(06): 443-457.
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276522
Gisbrecht A, Hammer B, Schleif F-M, Zhu X (2011)
Accelerating dissimilarity clustering for biomedical data analysis.
In: IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. pp.154-161.
PUB
Accelerating dissimilarity clustering for biomedical data analysis.
In: IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. pp.154-161.
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2091665
Zhu X, Hammer B (2011)
Patch Affinity Propagation.
Presented at the 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium.
PUB
Patch Affinity Propagation.
Presented at the 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium.
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276543
Gisbrecht A, Mokbel B, Hammer B (2010)
The Nystrom approximation for relational generative topographic mappings.
In: NIPS workshop on challenges of Data Visualization. .
PUB
The Nystrom approximation for relational generative topographic mappings.
In: NIPS workshop on challenges of Data Visualization. .
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994127
Villmann T, Haase S, Schleif F-M, Hammer B (2010)
Divergence Based Online Learning in Vector Quantization.
In: Artificial Intelligence and Soft Computing. Lecture Notes in Computer Science, 6113. Rutkowski L, Scherer R, Tadeusiewicz R, Zadeh L, Zurada J (Eds); Berlin, Heidelberg: Springer: 479-486.
PUB | DOI
Divergence Based Online Learning in Vector Quantization.
In: Artificial Intelligence and Soft Computing. Lecture Notes in Computer Science, 6113. Rutkowski L, Scherer R, Tadeusiewicz R, Zadeh L, Zurada J (Eds); Berlin, Heidelberg: Springer: 479-486.
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1796018
Arnonkijpanich B, Hasenfuss A, Hammer B (2010)
Local matrix learning in clustering and applications for manifold visualization.
Neural Networks 23(4): 476-486.
PUB | DOI | WoS | PubMed | Europe PMC
Local matrix learning in clustering and applications for manifold visualization.
Neural Networks 23(4): 476-486.
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993273
Arnonkijpanich B, Hammer B (2010)
Global Coordination based on Matrix Neural Gas for Dynamic Texture Synthesis.
In: ANNPR'2010. Lecture Notes in Artificial Intelligence, 5998. El Gayar N, Schwenker F (Eds); Springer: 84-95.
PUB
Global Coordination based on Matrix Neural Gas for Dynamic Texture Synthesis.
In: ANNPR'2010. Lecture Notes in Artificial Intelligence, 5998. El Gayar N, Schwenker F (Eds); Springer: 84-95.
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993367
Bunte K, Hammer B, Villmann T, Biehl M, Wismüller A (2010)
Exploratory Observation Machine (XOM) with Kullback-Leibler Divergence for Dimensionality Reduction and Visualization.
In: ESANN'10. Proceedings of the 18th European Symposium on Artificial Neural Networks. Verleysen M (Ed); Evere: D side: 87-92.
PUB
Exploratory Observation Machine (XOM) with Kullback-Leibler Divergence for Dimensionality Reduction and Visualization.
In: ESANN'10. Proceedings of the 18th European Symposium on Artificial Neural Networks. Verleysen M (Ed); Evere: D side: 87-92.
2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1929672
Witoelar AW, Ghosh A, de Vries JJG, Hammer B, Biehl M (2010)
Window-Based Example Selection in Learning Vector Quantization.
Neural Computing 22(11): 2924-2961.
PUB | DOI | WoS | PubMed | Europe PMC
Window-Based Example Selection in Learning Vector Quantization.
Neural Computing 22(11): 2924-2961.
2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1794373
Hammer B, Hasenfuss A (2010)
Topographic Mapping of Large Dissimilarity Data Sets.
Neural Computation 22(9): 2229-2284.
PUB | DOI | WoS | PubMed | Europe PMC
Topographic Mapping of Large Dissimilarity Data Sets.
Neural Computation 22(9): 2229-2284.
2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1795962
Schneider P, Bunte K, Stiekema H, Hammer B, Villmann T, Biehl M (2010)
Regularization in Matrix Relevance Learning.
IEEE Transactions on Neural Networks 21(5): 831-840.
PUB | DOI | WoS | PubMed | Europe PMC
Regularization in Matrix Relevance Learning.
IEEE Transactions on Neural Networks 21(5): 831-840.
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993978
Schleif F-M, Villmann T, Hammer B, Schneider P, Biehl M (2010)
Generalized derivative based Kernelized learning vector quantization.
In: Intelligent Data Engineering and Automated Learning – IDEAL 2010 11th International Conference, Paisley, UK, September 1-3, 2010. Proceedings. Fyfe C, Tino P, Charles D, Garcia-Osorio C, Yin H (Eds); Berlin u.a.: Springer: 21-28.
PUB | DOI
Generalized derivative based Kernelized learning vector quantization.
In: Intelligent Data Engineering and Automated Learning – IDEAL 2010 11th International Conference, Paisley, UK, September 1-3, 2010. Proceedings. Fyfe C, Tino P, Charles D, Garcia-Osorio C, Yin H (Eds); Berlin u.a.: Springer: 21-28.
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993536
Hammer B, Hasenfuss A (2010)
Clustering very large dissimilarity data sets.
In: Artificial Neural Networks in Pattern Recognition (ANNPR 2010). Proceedings. Schwenker F, El Gayar N (Eds); Lecture Notes in Artificial Intelligence, 5998. Berlin: Springer: 259-273.
PUB | DOI
Clustering very large dissimilarity data sets.
In: Artificial Neural Networks in Pattern Recognition (ANNPR 2010). Proceedings. Schwenker F, El Gayar N (Eds); Lecture Notes in Artificial Intelligence, 5998. Berlin: Springer: 259-273.
2010 | Konferenzband | Veröffentlicht | PUB-ID: 2276535
Hammer B, Hitzler P, Maass W, Toussaint M (Eds) (2010)
Learning paradigms in dynamic environments, 25.07.10-30.07.20.; 10302.
Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany.
PUB
Learning paradigms in dynamic environments, 25.07.10-30.07.20.; 10302.
Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany.
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276547
Mokbel B, Gisbrecht A, Hammer B (2010)
On the effect of clustering on quality assessment measures for dimensionality reduction.
In: NIPS workshop on Challenges of Data Visualization. .
PUB
On the effect of clustering on quality assessment measures for dimensionality reduction.
In: NIPS workshop on Challenges of Data Visualization. .
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993448
Gisbrecht A, Hammer B (2010)
Relevance learning in generative topographic maps.
In: ESANN'10. Verleysen M (Ed); Evere: D side: 387-392.
PUB
Relevance learning in generative topographic maps.
In: ESANN'10. Verleysen M (Ed); Evere: D side: 387-392.
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993452
Gisbrecht A, Mokbel B, Hammer B (2010)
Relational Generative Topographic Map.
In: ESANN'10. Verleysen M (Ed); Evere: D side: 277-282.
PUB
Relational Generative Topographic Map.
In: ESANN'10. Verleysen M (Ed); Evere: D side: 277-282.
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993457
Gisbrecht A, Mokbel B, Hasenfuss A, Hammer B (2010)
Visualizing Dissimilarity Data using generative topographic mapping.
In: KI'2010. Dillmann R, Beyerer J, Hanebeck UD, Schulz T (Eds); 227-237.
PUB
Visualizing Dissimilarity Data using generative topographic mapping.
In: KI'2010. Dillmann R, Beyerer J, Hanebeck UD, Schulz T (Eds); 227-237.
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994138
Villmann T, Haase S, Schleif F-M, Hammer B, Biehl M (2010)
The Mathematics of Divergence Based Online Learning in Vector Quanitzation.
In: ANNPR'2010. El Gayar N, Schwenker F (Eds); Berlin, Heidelberg: Springer: 108-119.
PUB
The Mathematics of Divergence Based Online Learning in Vector Quanitzation.
In: ANNPR'2010. El Gayar N, Schwenker F (Eds); Berlin, Heidelberg: Springer: 108-119.
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994227
Villmann T, Schleif F-M, Hammer B (2010)
Sparse representation of data.
In: ESANN'10. Verleysen M (Ed); D side: 225-234.
PUB
Sparse representation of data.
In: ESANN'10. Verleysen M (Ed); D side: 225-234.
2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993679
Hammer B, Schrauwen B, Steil JJ (2009)
Recent advances in efficient learning of recurrent networks.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); Brugge: d-facto: 213-226.
PUB
Recent advances in efficient learning of recurrent networks.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); Brugge: d-facto: 213-226.
2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993984
Schleif F-M, Villmann T, Kostrzewa M, Hammer B, Gammerman A (2009)
Cancer Informatics by Prototype-networks in Mass Spectrometry.
Artificial Intelligence in Medicine 45(2-3): 215-228.
PUB | DOI | WoS | PubMed | Europe PMC
Cancer Informatics by Prototype-networks in Mass Spectrometry.
Artificial Intelligence in Medicine 45(2-3): 215-228.
2009 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1994160
Villmann T, Hammer B, Biehl M (2009)
Some theoretical aspects of the neural gas vector quantizer.
In: Similarity Based Clustering. Biehl M, Hammer B, Verleysen M, Villmann T (Eds); Lecture Notes Artificial Intelligence, 5400, Berlin, Heidelberg: Springer: 23-34.
PUB | DOI
Some theoretical aspects of the neural gas vector quantizer.
In: Similarity Based Clustering. Biehl M, Hammer B, Verleysen M, Villmann T (Eds); Lecture Notes Artificial Intelligence, 5400, Berlin, Heidelberg: Springer: 23-34.
2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994305
Witolaer A, Biehl M, Hammer B (2009)
Equilibrium properties of offline LVQ.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); d-side publications: 535-540.
PUB
Equilibrium properties of offline LVQ.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); d-side publications: 535-540.
2009 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1993326
Biehl M, Hammer B, Schneider P, Villmann T (2009)
Metric learning for prototype based classification.
In: Innovations in Neural Information – Paradigms and Applications. Bianchini M, Maggini M, Scarselli F (Eds); Studies in Computational Intelligence, 247, Berlin: Springer: 183-199.
PUB | DOI
Metric learning for prototype based classification.
In: Innovations in Neural Information – Paradigms and Applications. Bianchini M, Maggini M, Scarselli F (Eds); Studies in Computational Intelligence, 247, Berlin: Springer: 183-199.
2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993994
Schneider P, Biehl M, Hammer B (2009)
Hyperparameter Learning in robust soft LVQ.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); d-side publications: 517-522.
PUB
Hyperparameter Learning in robust soft LVQ.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); d-side publications: 517-522.
2009 | Konferenzband | Veröffentlicht | PUB-ID: 1994310
Biehl M, Hammer B, Hochreiter S, Kremer SC, Villmann T (Eds) (2009)
Similarity-based learning on structures.; 9081.
Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany.
PUB
Similarity-based learning on structures.; 9081.
Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany.
2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994008
Schneider P, Biehl M, Hammer B (2009)
Distance learning in discriminative vector quantization.
Neural Computation 21(10): 2942-2969.
PUB | DOI | WoS | PubMed | Europe PMC
Distance learning in discriminative vector quantization.
Neural Computation 21(10): 2942-2969.
2009 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1993555
Hammer B, Hasenfuss A, Rossi F (2009)
Median topographic maps for biological data sets.
In: Similarity Based Clustering. Biehl M, Hammer B, Verleysen M, Villmann T (Eds); Lecture Notes Artificial Intelligence, 5400, Berlin, Heidelberg: Springer: 92-117.
PUB | DOI
Median topographic maps for biological data sets.
In: Similarity Based Clustering. Biehl M, Hammer B, Verleysen M, Villmann T (Eds); Lecture Notes Artificial Intelligence, 5400, Berlin, Heidelberg: Springer: 92-117.
2009 | Report | Veröffentlicht | PUB-ID: 1993316
Biehl M, Hammer B, Schleif F-M, Schneider P, Villmann T (2009)
Stationarity of Matrix Relevance Learning Vector Quantization. Machine Learning Reports.
Leipzig: Universität Leipzig.
PUB
Stationarity of Matrix Relevance Learning Vector Quantization. Machine Learning Reports.
Leipzig: Universität Leipzig.
2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993361
Bunte K, Hammer B, Biehl M (2009)
Nonlinear dimension reduction and visualization of labeled data.
In: International Conference on Computer Analysis of Images and Patterns. Jiang X, Petkov N (Eds); Lecture Notes in Computer Science, 5702, 5702. Berlin: Springer: 1162-1170.
PUB | DOI
Nonlinear dimension reduction and visualization of labeled data.
In: International Conference on Computer Analysis of Images and Patterns. Jiang X, Petkov N (Eds); Lecture Notes in Computer Science, 5702, 5702. Berlin: Springer: 1162-1170.
2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993429
Geweniger T, Zühlke D, Hammer B, Villmann T (2009)
Median variant of fuzzy-c-means.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); Evere: d-side publications: 523-528.
PUB
Median variant of fuzzy-c-means.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); Evere: d-side publications: 523-528.
2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993835
Mokbel B, Hasenfuss A, Hammer B (2009)
Graph-based Representation of Symbolic Musical Data.
In: Graph-Based Representation in Pattern Recognition (GbRPR 2009). Lecture Notes in Computer Science, 5534. Torsello A, Escolano F, Brun L, International Association for Pattern Recognition. Technical Committee on Graph Based Representations (Eds); Lecture notes in computer science, 5534. Berlin: Springer: 42-51.
PUB | DOI
Graph-based Representation of Symbolic Musical Data.
In: Graph-Based Representation in Pattern Recognition (GbRPR 2009). Lecture Notes in Computer Science, 5534. Torsello A, Escolano F, Brun L, International Association for Pattern Recognition. Technical Committee on Graph Based Representations (Eds); Lecture notes in computer science, 5534. Berlin: Springer: 42-51.
2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994004
Schneider P, Biehl M, Hammer B (2009)
Adaptive relevance matrices in learning vector quantization.
Neural Computation 21(12): 3532-3561.
PUB | DOI | WoS | PubMed | Europe PMC
Adaptive relevance matrices in learning vector quantization.
Neural Computation 21(12): 3532-3561.
2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993356
Bunte K, Biehl M, Hammer B (2009)
Nonlinear discriminative data visualization.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); Evere: d-side publications: 65-70.
PUB
Nonlinear discriminative data visualization.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); Evere: d-side publications: 65-70.
2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994152
Villmann T, Hammer B (2009)
Functional principal component learning using Oja's method and Sobolev norms.
In: Advances in Self-Organizing Maps. Principe JC, Miikkulainen R (Eds); 325-333.
PUB
Functional principal component learning using Oja's method and Sobolev norms.
In: Advances in Self-Organizing Maps. Principe JC, Miikkulainen R (Eds); 325-333.
2008 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1993939
Schleif F-M, Villmann T, Hammer B (2008)
Pattern Recognition by Supervised Relevance Neural Gas and its Application to Spectral Data in Bioinformatics.
In: Encyclopedia of Artificial Intelligence. Dopico JR-n R-al, Dorado J, Pazos A (Eds); IGI Global: 1337-1342.
PUB
Pattern Recognition by Supervised Relevance Neural Gas and its Application to Spectral Data in Bioinformatics.
In: Encyclopedia of Artificial Intelligence. Dopico JR-n R-al, Dorado J, Pazos A (Eds); IGI Global: 1337-1342.
2008 | Konferenzband | Veröffentlicht | PUB-ID: 1994329
de Raedt L, Hammer B, Hitzler P, Maass W (Eds) (2008)
Recurrent Neural Networks - Models, Capacities, and Applications.; 8041.
Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI).
PUB
Recurrent Neural Networks - Models, Capacities, and Applications.; 8041.
Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI).
2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993282
Arnonkijpanich B, Hammer B, Hasenfuss A, Lursinsap C (2008)
Matrix Learning for Topographic Neural Maps.
In: ICANN (1). Lecture Notes in Computer Science, 5163. Kurková V, Neruda R, Koutn'ık J (Eds); Berlin: Springer: 572-582.
PUB
Matrix Learning for Topographic Neural Maps.
In: ICANN (1). Lecture Notes in Computer Science, 5163. Kurková V, Neruda R, Koutn'ık J (Eds); Berlin: Springer: 572-582.
2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993261
Alex N, Hammer B (2008)
Parallelizing single pass patch clustering.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); Evere, Belgium: d-side publications: 227-232.
PUB
Parallelizing single pass patch clustering.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); Evere, Belgium: d-side publications: 227-232.
2008 | Report | Veröffentlicht | PUB-ID: 1993278
Arnonkijpanich B, Hammer B, Hasenfuss A (2008)
Local Matrix Adaptation in Topographic Neural Maps. IfI-08-07.
Clausthal-Zellerfeld: Clausthal University of Technology.
PUB
Local Matrix Adaptation in Topographic Neural Maps. IfI-08-07.
Clausthal-Zellerfeld: Clausthal University of Technology.
2008 | Report | Veröffentlicht | PUB-ID: 1993379
Bunte K, Schneider P, Hammer B, Schleif F-M, Villmann T, Biehl M (2008)
Discriminative Visualization by Limited Rank Matrix Learning. Machine Learning Reports.
Leipzig: Universität Leipzig.
PUB
Discriminative Visualization by Limited Rank Matrix Learning. Machine Learning Reports.
Leipzig: Universität Leipzig.
2008 | Report | Veröffentlicht | PUB-ID: 1994012
Schneider P, Biehl M, Hammer B (2008)
Matrix Adaptation in Discriminative Vector Quantization. IfI Technical Report Seriess.
Clausthal-Zellerfeld: Clausthal University of Technology.
PUB
Matrix Adaptation in Discriminative Vector Quantization. IfI Technical Report Seriess.
Clausthal-Zellerfeld: Clausthal University of Technology.
2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993788
Hasenfuss A, Hammer B (2008)
Single Pass Clustering and Classification of Large Dissimilarity Datasets.
In: Artificial Intelligence and Pattern Recognition. Prasad B, Sinha P, Ram A, Kerre EE (Eds); ISRST: 219-223.
PUB
Single Pass Clustering and Classification of Large Dissimilarity Datasets.
In: Artificial Intelligence and Pattern Recognition. Prasad B, Sinha P, Ram A, Kerre EE (Eds); ISRST: 219-223.
2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994072
Strickert M, Schneider P, Keilwagen J, Villmann T, Biehl M, Hammer B (2008)
Discriminatory Data Mapping by Matrix-Based Supervised Learning Metrics.
In: Artificial Neural Networks in Pattern Recognition. Third IAPR Workshop. Proceedings. Prevost L, Marinai S, Schwenker F (Eds); Lecture Notes in Computer Science, 5064, Berlin: Springer: 78-89.
PUB | DOI
Discriminatory Data Mapping by Matrix-Based Supervised Learning Metrics.
In: Artificial Neural Networks in Pattern Recognition. Third IAPR Workshop. Proceedings. Prevost L, Marinai S, Schwenker F (Eds); Lecture Notes in Computer Science, 5064, Berlin: Springer: 78-89.
2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994089
Strickert M, Sreenivasulu N, Villmann T, Hammer B (2008)
Robust Centroid-Based Clustering using Derivatives of Pearson Correlation.
In: BIOSIGNALS (2). Encarnação P, Veloso A (Eds); INSTICC - Institute for Systems and Technologies of Information, Control and Communication: 197-203.
PUB
Robust Centroid-Based Clustering using Derivatives of Pearson Correlation.
In: BIOSIGNALS (2). Encarnação P, Veloso A (Eds); INSTICC - Institute for Systems and Technologies of Information, Control and Communication: 197-203.
2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994281
Winkler T, Drieseberg J, Hasenfuß A, Hammer B, Hormann K (2008)
Thinning Mesh Animations.
In: Proceedings of Vision, Modeling, and Visualization 2008. Deussen O, Keim D, Saupe D (Eds); Konstanz, Germany: Aka: 149-158.
PUB
Thinning Mesh Animations.
In: Proceedings of Vision, Modeling, and Visualization 2008. Deussen O, Keim D, Saupe D (Eds); Konstanz, Germany: Aka: 149-158.
2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993804
Hasenfuss A, Hammer B, Rossi F (2008)
Patch Relational Neural Gas - Clustering of Huge Dissimilarity Datasets.
In: Artificial Neural Networks in Pattern Recognition, Third IAPR Workshop. Proceedings. Lecture Notes in Computer Science, 5064. Prevost L, Marinai S, Schwenker F (Eds); Berlin: Springer: 1-12.
PUB | DOI
Patch Relational Neural Gas - Clustering of Huge Dissimilarity Datasets.
In: Artificial Neural Networks in Pattern Recognition, Third IAPR Workshop. Proceedings. Lecture Notes in Computer Science, 5064. Prevost L, Marinai S, Schwenker F (Eds); Berlin: Springer: 1-12.
2008 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1993900
Schleif F-M, Hammer B, Villmann T (2008)
Analysis of Spectral Data in Clinical Proteomics by use of Learning Vector Quantizers.
In: Computational Intelligence in Biomedicine and Bioinformatics: Current Trends and Applications. Van de Werff M, Delder A, Tollenaar R (Eds); Berlin: Springer: 141-167.
PUB | DOI
Analysis of Spectral Data in Clinical Proteomics by use of Learning Vector Quantizers.
In: Computational Intelligence in Biomedicine and Bioinformatics: Current Trends and Applications. Van de Werff M, Delder A, Tollenaar R (Eds); Berlin: Springer: 141-167.
2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993798
Hasenfuss A, Hammer B, Geweniger T, Villmann T (2008)
Magnification Control in Relational Neural Gas.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); Brussels: d-side publications: 325-330.
PUB
Magnification Control in Relational Neural Gas.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); Brussels: d-side publications: 325-330.
2008 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994253
Villmann T, Schleif F-M, Kostrzewa M, Walch A, Hammer B (2008)
Classification of mass-spectrometric data in clinical proteomics using learning vector quantization methods.
Briefings in Bioinformatics 9(2): 129-143.
PUB | DOI | WoS | PubMed | Europe PMC
Classification of mass-spectrometric data in clinical proteomics using learning vector quantization methods.
Briefings in Bioinformatics 9(2): 129-143.
2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2001836
Geweniger T, Schleif F-M, Hasenfuss A, Hammer B, Villmann T (2008)
Comparison of cluster algorithms for the analysis of text data using Kolmogorov complexity.
In: ICONIP 2008. Köppen M, Kasabov NK, Coghill GG (Eds); Berlin, Heidelberg: Springer: 61-69.
PUB | DOI
Comparison of cluster algorithms for the analysis of text data using Kolmogorov complexity.
In: ICONIP 2008. Köppen M, Kasabov NK, Coghill GG (Eds); Berlin, Heidelberg: Springer: 61-69.
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993848
Rossi F, Hasenfuß A, Hammer B (2007)
Accelerating Relational Clustering Algorithms With Sparse Prototype Representation.
In: Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007). Bielefeld: Bielefeld University.
PUB
| PDF | DOI
Accelerating Relational Clustering Algorithms With Sparse Prototype Representation.
In: Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007). Bielefeld: Bielefeld University.
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994016
Schneider P, Biehl M, Schleif F-M, Hammer B (2007)
Advanced metric adaptation in Generalized LVQ for classification of mass spectrometry data.
In: Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007). Bielefeld: Bielefeld University.
PUB
| PDF | DOI
Advanced metric adaptation in Generalized LVQ for classification of mass spectrometry data.
In: Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007). Bielefeld: Bielefeld University.
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994267
Villmann T, Schleif F-M, Merenyi E, Strickert M, Hammer B (2007)
Class imaging of hyperspectral satellite remote sensing data using FLSOM.
In: Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007). Bielefeld: Bielefeld University.
PUB
| PDF | DOI
Class imaging of hyperspectral satellite remote sensing data using FLSOM.
In: Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007). Bielefeld: Bielefeld University.
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994295
Witoelar A, Biehl M, Hammer B (2007)
Learning Vector Quantization: generalization ability and dynamics of competing prototypes.
In: Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007). Bielefeld: Bielefeld University.
PUB
| PDF | DOI
Learning Vector Quantization: generalization ability and dynamics of competing prototypes.
In: Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007). Bielefeld: Bielefeld University.
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993782
Hasenfuss A, Hammer B (2007)
Relational topographic maps.
In: Advances in Intelligent Data Analysis VII, Proceedings of the 7th International Symposium on Intelligent Data Analysis. Berthold MR, Shawe-Taylor J, Lavrac N (Eds);4723. Berlin: Springer: 93-105.
PUB | DOI
Relational topographic maps.
In: Advances in Intelligent Data Analysis VII, Proceedings of the 7th International Symposium on Intelligent Data Analysis. Berthold MR, Shawe-Taylor J, Lavrac N (Eds);4723. Berlin: Springer: 93-105.
2007 | Report | Veröffentlicht | PUB-ID: 1993922
Schleif F-M, Hasenfuss A, Hammer B (2007)
Aggregation of multiple peak lists by use of an improved neural gas network.
Leipzig: Universität Leipzig.
PUB
Aggregation of multiple peak lists by use of an improved neural gas network.
Leipzig: Universität Leipzig.
2007 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993297
Biehl M, Ghosh A, Hammer B (2007)
Dynamics and generalization ability of LVQ algorithms.
Journal of Machine Learning Research 8: 323-360.
PUB
Dynamics and generalization ability of LVQ algorithms.
Journal of Machine Learning Research 8: 323-360.
2007 | Report | Veröffentlicht | PUB-ID: 1993533
Hammer B, Hasenfuss A (2007)
Relational topographic Maps. IfI Technical reports.
Clausthal-Zellerfeld: Clausthal University of Technology.
PUB
Relational topographic Maps. IfI Technical reports.
Clausthal-Zellerfeld: Clausthal University of Technology.
2007 | Report | Veröffentlicht | PUB-ID: 1993831
Melato M, Hammer B, Hormann K (2007)
Neural Gas for Surface Reconstruction. IfI Technical reports.
Clausthal-Zellerfeld: Clausthal University of Technology.
PUB
Neural Gas for Surface Reconstruction. IfI Technical reports.
Clausthal-Zellerfeld: Clausthal University of Technology.
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993970
Schleif F-M, Villmann T, Hammer B (2007)
Analysis of Proteomic Spectral Data by Multi Resolution Analysis and Self-Organizing-Maps.
In: Application of Fuzzy Sets Theory. Proceedings of the 7th International Workshop on Fuzzy Logic and Applications. LNAI 4578. Masulli F, Mitra S, Pasi G (Eds); Berlin, Heidelberg: Springer: 563-570.
PUB | DOI
Analysis of Proteomic Spectral Data by Multi Resolution Analysis and Self-Organizing-Maps.
In: Application of Fuzzy Sets Theory. Proceedings of the 7th International Workshop on Fuzzy Logic and Applications. LNAI 4578. Masulli F, Mitra S, Pasi G (Eds); Berlin, Heidelberg: Springer: 563-570.
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993999
Schneider P, Biehl M, Hammer B (2007)
Relevance matrices in LVQ.
In: Proc. Of European Symposium on Artificial Neural Networks. Verleysen M (Ed); Brussels, Belgium: d-side publications: 37-42.
PUB
Relevance matrices in LVQ.
In: Proc. Of European Symposium on Artificial Neural Networks. Verleysen M (Ed); Brussels, Belgium: d-side publications: 37-42.
2007 | Report | Veröffentlicht | PUB-ID: 1993334
Blazewicz J, Ecker K, Hammer B (2007)
ICOLE-2007, German-Polish Workshop on Computational Biology, Scheduling and Machine Learning. Lessach, Austria, 27.05.-02.06.2007.
Clausthal-Zellerfeld: Clausthal University of Technology.
PUB
ICOLE-2007, German-Polish Workshop on Computational Biology, Scheduling and Machine Learning. Lessach, Austria, 27.05.-02.06.2007.
Clausthal-Zellerfeld: Clausthal University of Technology.
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993746
Hammer B, Villmann T (2007)
How to process uncertainty in machine learning.
In: Proc. Of European Symposium on Artificial Neural Networks (ESANN'2007). Verleysen M (Ed); Brussels, Belgium: d-side publications: 79-90.
PUB
How to process uncertainty in machine learning.
In: Proc. Of European Symposium on Artificial Neural Networks (ESANN'2007). Verleysen M (Ed); Brussels, Belgium: d-side publications: 79-90.
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993811
Hasenfuss A, Hammer B, Schleif F-M, Villmann T (2007)
Neural gas clustering for dissimilarity data with continuous prototypes.
In: Computational and Ambient Intelligence – Proceedings of the 9th Work-conference on Artificial Neural Networks. LNCS 4507. Sandoval F, Prieto A, Cabestany J, Grana M (Eds); Berlin: Springer: 539-546.
PUB | DOI
Neural gas clustering for dissimilarity data with continuous prototypes.
In: Computational and Ambient Intelligence – Proceedings of the 9th Work-conference on Artificial Neural Networks. LNCS 4507. Sandoval F, Prieto A, Cabestany J, Grana M (Eds); Berlin: Springer: 539-546.
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994299
Witolaer A, Biehl M, Ghosh A, Hammer B (2007)
On the dynamics of vector quantization and neural gas.
In: Proc. Of European Symposium on Artificial Neural Networks (ESANN'2007). Verleysen M (Ed); Brussels, Belgium: d-side publications: 127-132.
PUB
On the dynamics of vector quantization and neural gas.
In: Proc. Of European Symposium on Artificial Neural Networks (ESANN'2007). Verleysen M (Ed); Brussels, Belgium: d-side publications: 127-132.
2007 | Konferenzband | Veröffentlicht | PUB-ID: 1994321
Biehl M, Hammer B, Verleysen M, Villmann T (Eds) (2007)
Similarity-based Clustering and its Application to Medicine and Biology.; 7131.
Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI).
PUB
Similarity-based Clustering and its Application to Medicine and Biology.; 7131.
Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI).
2007 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1993630
Hammer B, Micheli A, Sperduti A (2007)
Adaptive Contextual Processing of Structured Data by Recursive Neural Networks: A Survey of Computational Properties.
In: Perspectives of Neural-Symbolic Integration. Hammer B, Hitzler P (Eds); Studies in computational Intelligence, 77, Berlin: Springer: 67-94.
PUB | DOI
Adaptive Contextual Processing of Structured Data by Recursive Neural Networks: A Survey of Computational Properties.
In: Perspectives of Neural-Symbolic Integration. Hammer B, Hitzler P (Eds); Studies in computational Intelligence, 77, Berlin: Springer: 67-94.
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993820
Hasenfuss A, Hammer B, Schleif F-M, Villmann T (2007)
Neural gas clustering for sparse proximity data.
In: Proceedings of the 9th International Work-Conference on Artificial Neural Networks.LNCS 4507. Sandoval F, Prieto A, Cabestany J, Grana M (Eds); Berlin, Heidelberg, Germany: Springer: 539-546.
PUB
Neural gas clustering for sparse proximity data.
In: Proceedings of the 9th International Work-Conference on Artificial Neural Networks.LNCS 4507. Sandoval F, Prieto A, Cabestany J, Grana M (Eds); Berlin, Heidelberg, Germany: Springer: 539-546.
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993907
Schleif F-M, Hammer B, Villmann T (2007)
Supervised Neural Gas for Functional Data and its Application to the Analysis of Clinical Proteom Spectra.
In: Computational and Ambient Intelligence. Proceedings of the 9th International Work-Conference on Artificial Neural Networks. LNCS, 4507. Sandoval F, Prieto A, Cabestany J, Grana M (Eds); Berlin, Heidelberg: Springer: 1036-1044.
PUB | DOI
Supervised Neural Gas for Functional Data and its Application to the Analysis of Clinical Proteom Spectra.
In: Computational and Ambient Intelligence. Proceedings of the 9th International Work-Conference on Artificial Neural Networks. LNCS, 4507. Sandoval F, Prieto A, Cabestany J, Grana M (Eds); Berlin, Heidelberg: Springer: 1036-1044.
2007 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1994102
Tino P, Hammer B, Boden M (2007)
Markovian Bias of Neural-based Architectures With Feedback Connections.
In: Perspectives of Neural-Symbolic Integration. Hammer B, Hitzler P (Eds); Studies in computational Intelligence, 77, Berlin: Springer: 95-134.
PUB | DOI
Markovian Bias of Neural-based Architectures With Feedback Connections.
In: Perspectives of Neural-Symbolic Integration. Hammer B, Hitzler P (Eds); Studies in computational Intelligence, 77, Berlin: Springer: 95-134.
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994258
Villmann T, Schleif F-M, Merenyi E, Hammer B (2007)
Fuzzy Labeled Self Organizing Map for Clasification of Spectra.
In: Computational and Ambient Intelligence. Proceedings of the 9th Work-conference on Artificial Neural Networks. LNCS, 4507. Sandoval F, Prieto A, Cabestany J, Grana M (Eds); Berlin: Springer: 556-563.
PUB | DOI
Fuzzy Labeled Self Organizing Map for Clasification of Spectra.
In: Computational and Ambient Intelligence. Proceedings of the 9th Work-conference on Artificial Neural Networks. LNCS, 4507. Sandoval F, Prieto A, Cabestany J, Grana M (Eds); Berlin: Springer: 556-563.
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993895
Schleif F-M, Hammer B, Villmann T (2006)
Margin based Active Learning for LVQ Networks.
In: Proc. Of European Symposium on Artificial Neural Networks. Verleysen M (Ed); Brussels, Belgium: d-side publications: 539-544.
PUB
Margin based Active Learning for LVQ Networks.
In: Proc. Of European Symposium on Artificial Neural Networks. Verleysen M (Ed); Brussels, Belgium: d-side publications: 539-544.
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994184
Villmann T, Hammer B, Schleif F-M, Geweniger T, Fischer T, Cottrell M (2006)
Prototype based classification using information theoretic learning.
In: Neural Information Processing, 13th International Conference. Proceedings. King I, Wang J, Chan L, Wang DLL (Eds); Lecture Notes in Computer Science, 4233, Part II. Berlin: Springer: 40-49.
PUB
Prototype based classification using information theoretic learning.
In: Neural Information Processing, 13th International Conference. Proceedings. King I, Wang J, Chan L, Wang DLL (Eds); Lecture Notes in Computer Science, 4233, Part II. Berlin: Springer: 40-49.
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994273
Villmann T, Seiffert U, Schleif F-M, Brüß C, Geweniger T, Hammer B (2006)
Fuzzy Labeled Self-Organizing Map with Label-Adjusted Prototypes.
In: Proceedings of Conference Artificial Neural Networks in Pattern Recognition. Schwenker F (Ed); Berlin: Springer: 46-56.
PUB | DOI
Fuzzy Labeled Self-Organizing Map with Label-Adjusted Prototypes.
In: Proceedings of Conference Artificial Neural Networks in Pattern Recognition. Schwenker F (Ed); Berlin: Springer: 46-56.
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993889
Schleif F-M, Elssner T, Kostrzewa M, Villmann T, Hammer B (2006)
Machine Learning and Soft-Computing in Bioinformatics. A Short Journey.
In: Proc. of FLINS 2006. World Scientific Press: 541-548.
PUB
Machine Learning and Soft-Computing in Bioinformatics. A Short Journey.
In: Proc. of FLINS 2006. World Scientific Press: 541-548.
2006 | Report | Veröffentlicht | PUB-ID: 1993322
Biehl M, Hammer B, Schneider P (2006)
Matrix Learning in Learning Vector Quantization.
Clausthal-Zellerfeld: Clausthal University of Technology.
PUB
Matrix Learning in Learning Vector Quantization.
Clausthal-Zellerfeld: Clausthal University of Technology.
2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993391
Cottrell M, Hammer B, Hasenfuss A, Villmann T (2006)
Batch and Median Neural Gas.
Neural Networks 19(6-7): 762-771.
PUB | DOI | WoS | PubMed | Europe PMC
Batch and Median Neural Gas.
Neural Networks 19(6-7): 762-771.
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993568
Hammer B, Hasenfuss A, Schleif F-M, Villmann T (2006)
Supervised median neural gas.
In: Smart Engineering System Design. Intelligent Engineering Systems Through Artificial Neural Networks, 16. Dagli C, Buczak A, Enke D, Embrechts A, Ersoy O (Eds); ASME Press: 623-633.
PUB
Supervised median neural gas.
In: Smart Engineering System Design. Intelligent Engineering Systems Through Artificial Neural Networks, 16. Dagli C, Buczak A, Enke D, Embrechts A, Ersoy O (Eds); ASME Press: 623-633.
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993594
Hammer B, Hasenfuss A, Schleif F-M, Villmann T (2006)
Supervised median clustering.
In: Smart systems engineering : infra-structure systems engineering, bio-informatics and computational biology and evolutionary computation : proceedings of the Artificial Neural Networks in Engineering Conference (ANNIE 2006). Dagli CH (Ed); ASME Press series on intelligent engineering systems through artificial neural networks, 16, New York, NY: ASME Press: 623-632.
PUB
Supervised median clustering.
In: Smart systems engineering : infra-structure systems engineering, bio-informatics and computational biology and evolutionary computation : proceedings of the Artificial Neural Networks in Engineering Conference (ANNIE 2006). Dagli CH (Ed); ASME Press series on intelligent engineering systems through artificial neural networks, 16, New York, NY: ASME Press: 623-632.
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993878
Schleif F-M, Elssner T, Kostrzewa M, Villmann T, Hammer B (2006)
Analysis and Visualization of Proteomic Data by Fuzzy labeled Self-Organizing Maps.
In: 19th IEEE International Symposium on Computer- based Medical Systems. Lee DJ, Nutter B, Antani S, Mitra S, Archibald J (Eds); Los Alamitos: IEEE Computer Society Press: 919-924.
PUB | DOI
Analysis and Visualization of Proteomic Data by Fuzzy labeled Self-Organizing Maps.
In: 19th IEEE International Symposium on Computer- based Medical Systems. Lee DJ, Nutter B, Antani S, Mitra S, Archibald J (Eds); Los Alamitos: IEEE Computer Society Press: 919-924.
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994028
Seiffert U, Hammer B, Kaski S, Villmann T (2006)
Neural Networks and Machine Learning in Bioinformatics - Theory and Applications.
In: Proc. Of European Symposium on Artificial Neural Networks. Verleysen M (Ed); Brussels, Belgium: d-side publications: 521-532.
PUB
Neural Networks and Machine Learning in Bioinformatics - Theory and Applications.
In: Proc. Of European Symposium on Artificial Neural Networks. Verleysen M (Ed); Brussels, Belgium: d-side publications: 521-532.
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994201
Villmann T, Hammer B, Seiffert U (2006)
Perspectives of Self-adapted Self-organizing Clustering in Organic Computing.
In: Biologically Inspired Approaches to Advanced Information Technology, Second International Workshop. Proceedings. Lecture Notes in Computer Science, 3853. Ijspeert AJ, Masuzawa T, Kusumoto S (Eds); Berlin: Springer: 141-159.
PUB | DOI
Perspectives of Self-adapted Self-organizing Clustering in Organic Computing.
In: Biologically Inspired Approaches to Advanced Information Technology, Second International Workshop. Proceedings. Lecture Notes in Computer Science, 3853. Ijspeert AJ, Masuzawa T, Kusumoto S (Eds); Berlin: Springer: 141-159.
2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994237
Villmann T, Schleif F-M, Hammer B (2006)
Comparison of relevance learning vector quantization with other metric adaptive classification methods.
Neural Networks 19(5): 610-622.
PUB | DOI | WoS | PubMed | Europe PMC
Comparison of relevance learning vector quantization with other metric adaptive classification methods.
Neural Networks 19(5): 610-622.
2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993440
Ghosh A, Biehl M, Hammer B (2006)
Performance analysis of LVQ algorithms: a statistical physics approach.
Neural Networks 19(6-7): 817-829.
PUB | DOI | WoS | PubMed | Europe PMC
Performance analysis of LVQ algorithms: a statistical physics approach.
Neural Networks 19(6-7): 817-829.
2006 | Report | Veröffentlicht | PUB-ID: 1993584
Hammer B, Hasenfuss A, Schleif F-M, Villmann T (2006)
Supervised median clustering. IfI Technical reports.
Clausthal-Zellerfeld: Clausthal University of Technology.
PUB
Supervised median clustering. IfI Technical reports.
Clausthal-Zellerfeld: Clausthal University of Technology.
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993611
Hammer B, Hasenfuss A, Villmann T (2006)
Magnification Control for Batch Neural Gas.
In: Proc. Of European Symposium on Artificial Neural Networks. Verleysen M (Ed); Brussels: d-side publications: 7-12.
PUB
Magnification Control for Batch Neural Gas.
In: Proc. Of European Symposium on Artificial Neural Networks. Verleysen M (Ed); Brussels: d-side publications: 7-12.
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993659
Hammer B, Neubauer N (2006)
On the capacity of unsupervised recursive neural networks for symbol processing.
In: Workshop proceedings of NeSy'06. d'Avila Garcez A, Hitzler P, Tamburrini G (Eds);.
PUB
On the capacity of unsupervised recursive neural networks for symbol processing.
In: Workshop proceedings of NeSy'06. d'Avila Garcez A, Hitzler P, Tamburrini G (Eds);.
2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993762
Hammer B, Villmann T (2006)
Effizient Klassifizieren und Clustern: Lernparadigmen von Vektorquantisierern.
Künstliche Intelligenz 3(6): 5-11.
PUB
Effizient Klassifizieren und Clustern: Lernparadigmen von Vektorquantisierern.
Künstliche Intelligenz 3(6): 5-11.
2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994195
Villmann T, Hammer B, Schleif F-M, Geweniger T, Herrmann W (2006)
Fuzzy Classification by Fuzzy Labeled Neural Gas.
Neural Networks 19(6-7): 772-779.
PUB | DOI | WoS | PubMed | Europe PMC
Fuzzy Classification by Fuzzy Labeled Neural Gas.
Neural Networks 19(6-7): 772-779.
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2017225
Hammer B, Villmann T, Schleif F-M, Albani C, Hermann W (2006)
Learning vector quantization classification with local relevance determination for medical data.
In: Artificial Intelligence and Soft-Computing - Proceedings of ICAISC 2006. LNAI, 4029. Rutkowski L, Tadeusiewicz R, Zadeh LA, Zurada J (Eds); Lecture notes in computer science ; 4029 : Lecture notes in artificial intelligence, 4029. Berlin, Heidelberg: Springer: 603-612.
PUB | DOI
Learning vector quantization classification with local relevance determination for medical data.
In: Artificial Intelligence and Soft-Computing - Proceedings of ICAISC 2006. LNAI, 4029. Rutkowski L, Tadeusiewicz R, Zadeh LA, Zurada J (Eds); Lecture notes in computer science ; 4029 : Lecture notes in artificial intelligence, 4029. Berlin, Heidelberg: Springer: 603-612.
2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993624
Hammer B, Micheli A, Neubauer N, Sperduti A, Strickert M (2005)
Self Organizing Maps for Time Series.
In: Proceedings of WSOM 2005. 115-122.
PUB
Self Organizing Maps for Time Series.
In: Proceedings of WSOM 2005. 115-122.
2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994172
Villmann T, Hammer B, Schleif F-M, Geweniger T (2005)
Fuzzy Labeled Neural GAS for Fuzzy Classification.
In: Proceedings of the 5th Workshop on Self-Organizing Maps [on CD-ROM]. Cottrell M (Ed); Paris, France: University Paris-1-Pantheon-Sorbonne: 283-290.
PUB
Fuzzy Labeled Neural GAS for Fuzzy Classification.
In: Proceedings of the 5th Workshop on Self-Organizing Maps [on CD-ROM]. Cottrell M (Ed); Paris, France: University Paris-1-Pantheon-Sorbonne: 283-290.
2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993305
Biehl M, Gosh A, Hammer B (2005)
The dynamics of Learning Vector Quantization.
In: ESANN'05. Verleysen M (Ed); Evere: d-side publishing: 13-18.
PUB
The dynamics of Learning Vector Quantization.
In: ESANN'05. Verleysen M (Ed); Evere: d-side publishing: 13-18.
2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993386
Cottrell M, Hammer B, Hasenfuss A, Villmann T (2005)
Batch NG.
In: Proceedings of WSOM 2005. 275-282.
PUB
Batch NG.
In: Proceedings of WSOM 2005. 275-282.
2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993444
Ghosh A, Biehl M, Hammer B (2005)
Dynamical Analysis of LVQ type learning rules.
In: Proceedings of WSOM. 578-594.
PUB
Dynamical Analysis of LVQ type learning rules.
In: Proceedings of WSOM. 578-594.
2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993665
Hammer B, Rechtien A, Strickert M, Villmann V (2005)
Relevance learning for mental disease classification.
In: ESANN'05. Verleysen M (Ed); d-side publishing: 139-144.
PUB
Relevance learning for mental disease classification.
In: ESANN'05. Verleysen M (Ed); d-side publishing: 139-144.
2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994118
Tluk von Toschanowitz K, Hammer B, Ritter H (2005)
Relevance determination in reinforcement learning.
In: ESANN'05. Verleysen M (Ed); d-side publishing: 369-374.
PUB
Relevance determination in reinforcement learning.
In: ESANN'05. Verleysen M (Ed); d-side publishing: 369-374.
2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994219
Villmann T, Schleif F-M, Hammer B (2005)
Fuzzy Classification for Classification of Mass Spectrometric Data Based on Learning Vector Quantization.
In: International Workshop on Integrative Bioinformatics. .
PUB
Fuzzy Classification for Classification of Mass Spectrometric Data Based on Learning Vector Quantization.
In: International Workshop on Integrative Bioinformatics. .
2005 | Report | Veröffentlicht | PUB-ID: 1993675
Hammer B, Schleif F-M, Villmann T (2005)
On the Generalization Ability of Prototype-Based Classifiers with Local Relevance Determination. IfI Technical reports.
Clausthal-Zellerfeld: Clausthal University of Technology.
PUB
On the Generalization Ability of Prototype-Based Classifiers with Local Relevance Determination. IfI Technical reports.
Clausthal-Zellerfeld: Clausthal University of Technology.
2005 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993671
Hammer B, Saunders C, Sperduti A (2005)
Special issue on neural networks and kernel methods for structured domains.
Neural Networks 18(8): 1015-1018.
PUB | DOI | WoS | PubMed | Europe PMC
Special issue on neural networks and kernel methods for structured domains.
Neural Networks 18(8): 1015-1018.
2005 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1993710
Hammer B, Strickert M, Villmann T (2005)
Prototype based recognition of splice sites.
In: Bioinformatics using computational intelligence paradigms. Seiffert U, Jain LC, Schweitzer P (Eds); Berlin: Springer: 25-55.
PUB
Prototype based recognition of splice sites.
In: Bioinformatics using computational intelligence paradigms. Seiffert U, Jain LC, Schweitzer P (Eds); Berlin: Springer: 25-55.
2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993974
Schleif F-M, Villmann T, Hammer B (2005)
Local Metric Adaptation for Soft Nearest Prototype Classification to Classify Proteomic Data.
In: Proceedings of the 6th Workshop on Fuzzy Logic and Applications. Bloch I, Petrosino A, Tettamanzi AGB (Eds); Berlin, Heidelberg: Springer: 290-296.
PUB | DOI
Local Metric Adaptation for Soft Nearest Prototype Classification to Classify Proteomic Data.
In: Proceedings of the 6th Workshop on Fuzzy Logic and Applications. Bloch I, Petrosino A, Tettamanzi AGB (Eds); Berlin, Heidelberg: Springer: 290-296.
2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994249
Villmann T, Schleif F-M, Hammer B (2005)
Fuzzy labeled soft nearest neighbor classification with relevance learning.
In: Proceedings of the International Conference of Machine Learning Applications. Wani MA, Cios KJ, Hafeez K (Eds); Los Angeles: IEEE Press: 11-15.
PUB
Fuzzy labeled soft nearest neighbor classification with relevance learning.
In: Proceedings of the International Conference of Machine Learning Applications. Wani MA, Cios KJ, Hafeez K (Eds); Los Angeles: IEEE Press: 11-15.
2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993750
Hammer B, Villmann T (2005)
Classification using non standard metrics.
In: ESANN'05. Verleysen M (Ed); Brussels: d-side publishing: 303-316.
PUB
Classification using non standard metrics.
In: ESANN'05. Verleysen M (Ed); Brussels: d-side publishing: 303-316.
2004 | Report | Veröffentlicht | PUB-ID: 1993732
Hammer B, Tino P, Micheli A (2004)
A mathematical characterization of the architectural bias of recursive models. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
PUB
A mathematical characterization of the architectural bias of recursive models. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994168
Villmann T, Hammer B, Schleif F-M (2004)
Metrik Adaptation for Optimal Feature Classification in Learning Vector Quantization Applied to Environment Detection.
In: Proceedings of Selbstorganisation Von Adaptivem Verfahren. Fortschritts-Berichte VDI Reihe 10, Nr. 742. Groß H-M, Debes K, Böhme H-J (Eds); VDI Verlag: 592-597.
PUB
Metrik Adaptation for Optimal Feature Classification in Learning Vector Quantization Applied to Environment Detection.
In: Proceedings of Selbstorganisation Von Adaptivem Verfahren. Fortschritts-Berichte VDI Reihe 10, Nr. 742. Groß H-M, Debes K, Böhme H-J (Eds); VDI Verlag: 592-597.
2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994111
Tluk von Toschanowitz K, Hammer B, Ritter H (2004)
Mapping the Design Space of Reinforcement Learning Problems - a Case Study.
In: SOAVE 2004, 3rd Workshop on SelfOrganization of AdaptiVE Behavior. Gross H-M, Debes K, Böhme H-J (Eds); VDI Verlag: 251-261.
PUB
Mapping the Design Space of Reinforcement Learning Problems - a Case Study.
In: SOAVE 2004, 3rd Workshop on SelfOrganization of AdaptiVE Behavior. Gross H-M, Debes K, Böhme H-J (Eds); VDI Verlag: 251-261.
2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994212
Villmann T, Schleif F-M, Hammer B (2004)
Metric adaptation for optimal feature classification in learning vector quantization applied to environment detection.
In: SOAVE 2004, 3rd Workshop on SelfOrganization of AdaptiVE Behavior. Groß H-M, Debes K, Böhme H-J (Eds); VDI Verlag.
PUB
Metric adaptation for optimal feature classification in learning vector quantization applied to environment detection.
In: SOAVE 2004, 3rd Workshop on SelfOrganization of AdaptiVE Behavior. Groß H-M, Debes K, Böhme H-J (Eds); VDI Verlag.
2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993620
Hammer B, Jain BJ (2004)
Neural methods for non-standard data.
In: European Symposium on Artificial Neural Networks'2004. Verleysen M (Ed); D-side publications: 281-292.
PUB
Neural methods for non-standard data.
In: European Symposium on Artificial Neural Networks'2004. Verleysen M (Ed); D-side publications: 281-292.
2004 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993649
Hammer B, Micheli A, Sperduti A, Strickert M (2004)
Recursive self-organizing network models.
Neural Networks 17(8-9): 1061-1085.
PUB | DOI | WoS | PubMed | Europe PMC
Recursive self-organizing network models.
Neural Networks 17(8-9): 1061-1085.
2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993702
Hammer B, Strickert M, Villmann T (2004)
Relevance LVQ versus SVM.
In: Artificial Intelligence and Softcomputing, Lecture Notes in Artificial Intelligence, 3070. Rutkowski L, Siekmann J, Tadeusiewicz R, Zadeh LA (Eds); Berlin: Springer: 592-597.
PUB
Relevance LVQ versus SVM.
In: Artificial Intelligence and Softcomputing, Lecture Notes in Artificial Intelligence, 3070. Rutkowski L, Siekmann J, Tadeusiewicz R, Zadeh LA (Eds); Berlin: Springer: 592-597.
2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993419
Gersmann K, Hammer B (2004)
A reinforcement learning algorithm to improve scheduling search heuristics with the SVM.
In: IJCNN. .
PUB
A reinforcement learning algorithm to improve scheduling search heuristics with the SVM.
In: IJCNN. .
2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993870
Schleif F-M, Clauss U, Villmann T, Hammer B (2004)
Supervised Relevance Neural Gas and Unified Maximum Separability Analysis for Classification of Mass Spectrometric Data.
In: Proceedings of the 3rd International Conference on Machine Learning and Applications (ICMLA) 2004. Wani MA, Cios KJ, Hafeez K (Eds); Los Alamitos, CA, USA: IEEE Press: 374-379.
PUB
Supervised Relevance Neural Gas and Unified Maximum Separability Analysis for Classification of Mass Spectrometric Data.
In: Proceedings of the 3rd International Conference on Machine Learning and Applications (ICMLA) 2004. Wani MA, Cios KJ, Hafeez K (Eds); Los Alamitos, CA, USA: IEEE Press: 374-379.
2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994049
Strickert M, Hammer B (2004)
Self-organizing context learning.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); D-side publications: 39-44.
PUB
Self-organizing context learning.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); D-side publications: 39-44.
2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994099
Tino P, Hammer B (2004)
On early stages of learning in connectionist models with feedback connections.
In: Compositional Connectionism in Cognitive Science. .
PUB
On early stages of learning in connectionist models with feedback connections.
In: Compositional Connectionism in Cognitive Science. .
2003 | Report | Veröffentlicht | PUB-ID: 1993725
Hammer B, Strickert M, Villmann T (2003)
On the generalization ability of GRLVQ. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
PUB
On the generalization ability of GRLVQ. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
2003 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994108
Tiño P, Hammer B (2003)
Architectural Bias in Recurrent Neural Networks: Fractal Analysis.
Neural Computation 15(8): 1931-1957.
PUB
Architectural Bias in Recurrent Neural Networks: Fractal Analysis.
Neural Computation 15(8): 1931-1957.
2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994223
Villmann T, Schleif F-M, Hammer B (2003)
Supervised Neural Gas and Relevance Learning in Learning Vector Quantization.
In: Proceedings of the 4th Workshop on Self Organizing Maps [on CD-ROM]. Yamakawa T (Ed); Hibikino, Kitakyushu, Japan: Kyushu Institute of Technology: 47-52.
PUB
Supervised Neural Gas and Relevance Learning in Learning Vector Quantization.
In: Proceedings of the 4th Workshop on Self Organizing Maps [on CD-ROM]. Yamakawa T (Ed); Hibikino, Kitakyushu, Japan: Kyushu Institute of Technology: 47-52.
2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993338
Bojer T, Hammer B, Koeers C (2003)
Monitoring technical systems with prototype based clustering.
In: ESANN 2003, 10th European Symposium on Artificial Neural Network. Proceedings. Verleysen M (Ed); Evere: D-side publication: 433-439.
PUB
Monitoring technical systems with prototype based clustering.
In: ESANN 2003, 10th European Symposium on Artificial Neural Network. Proceedings. Verleysen M (Ed); Evere: D-side publication: 433-439.
2003 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993530
Hammer B, Gersmann K (2003)
A Note on the Universal Approximation Capability of Support Vector Machines.
Neural Processing Letters 17(1): 43-53.
PUB
A Note on the Universal Approximation Capability of Support Vector Machines.
Neural Processing Letters 17(1): 43-53.
2003 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1993487
Hammer B (2003)
Perspectives on learning symbolic data with connectionistic systems.
In: Adaptivity and Learning. Kühn R, Menzel R, Menzel W, Ratsch U, Richter MM, Stamatescu I (Eds); Berlin: Springer: 141-160.
PUB
Perspectives on learning symbolic data with connectionistic systems.
In: Adaptivity and Learning. Kühn R, Menzel R, Menzel W, Ratsch U, Richter MM, Stamatescu I (Eds); Berlin: Springer: 141-160.
2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993754
Hammer B, Villmann T (2003)
Mathematical Aspects of Neural Networks.
In: Proc. Of European Symposium on Artificial Neural Networks (ESANN'2003). Verleysen M (Ed); Brussels, Belgium: d-side: 59-72.
PUB
Mathematical Aspects of Neural Networks.
In: Proc. Of European Symposium on Artificial Neural Networks (ESANN'2003). Verleysen M (Ed); Brussels, Belgium: d-side: 59-72.
2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994053
Strickert M, Hammer B (2003)
Unsupervised recursive sequence processing.
In: 10th European Symposium on Artificial Neural Networks. Proceedings. Verleysen M (Ed); D-side publication: 27-32.
PUB
Unsupervised recursive sequence processing.
In: 10th European Symposium on Artificial Neural Networks. Proceedings. Verleysen M (Ed); D-side publication: 27-32.
2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994060
Strickert M, Hammer B (2003)
Neural Gas for Sequences.
In: WSOM'03. 53-57.
PUB
Neural Gas for Sequences.
In: WSOM'03. 53-57.
2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993412
Gersmann K, Hammer B (2003)
Improving iterative repair strategies for scheduling with the SVM.
In: ESANN 2003, 10th European Symposium on Artificial Neural Networks. Proceedings. Verleysen M (Ed); Evere: D-side publication: 235-240.
PUB
Improving iterative repair strategies for scheduling with the SVM.
In: ESANN 2003, 10th European Symposium on Artificial Neural Networks. Proceedings. Verleysen M (Ed); Evere: D-side publication: 235-240.
2003 | Report | Veröffentlicht | PUB-ID: 1993645
Hammer B, Micheli a., Sperduti A (2003)
A general framework for self-organizing structure processing neural networks.
Pisa: Universita di Pisa, Dipartimento die Informatica.
PUB
A general framework for self-organizing structure processing neural networks.
Pisa: Universita di Pisa, Dipartimento die Informatica.
2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993349
Bojer T, Hammer B, Strickert M, Villmann T (2003)
Determining Relevant Input Dimensions for the Self-Organizing Map.
In: Neural Networks and Soft Computing (Proc. ICNNSC 2002). Rutkowski L, Kacprzyk J (Eds); Physica-Verlag: 388-393.
PUB
Determining Relevant Input Dimensions for the Self-Organizing Map.
In: Neural Networks and Soft Computing (Proc. ICNNSC 2002). Rutkowski L, Kacprzyk J (Eds); Physica-Verlag: 388-393.
2003 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993736
Hammer B, Tiño P (2003)
Recurrent Neural Networks with Small Weights Implement Definite Memory Machines.
Neural Computation 15(8): 1897-1929.
PUB
Recurrent Neural Networks with Small Weights Implement Definite Memory Machines.
Neural Computation 15(8): 1897-1929.
2003 | Report | Veröffentlicht | PUB-ID: 1994157
Villmann T, Hammer B (2003)
Metric adaptation and relevance learning in learning vector quantization. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
PUB
Metric adaptation and relevance learning in learning vector quantization. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
2003 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994208
Villmann T, Merényi E, Hammer B (2003)
Neural maps in remote sensing image analysis.
Neural Networks 16(3-4): 389-403.
PUB
Neural maps in remote sensing image analysis.
Neural Networks 16(3-4): 389-403.
2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993636
Hammer B, Micheli A, Sperduti A (2002)
A general framework for unsupervised processing of structured data.
In: ESANN 2002, 10th European Symposium on Artificial Neural Network. Proceedings. Verleysen M (Ed); De-side publication: 389-394.
PUB
A general framework for unsupervised processing of structured data.
In: ESANN 2002, 10th European Symposium on Artificial Neural Network. Proceedings. Verleysen M (Ed); De-side publication: 389-394.
2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994095
Tino P, Hammer B (2002)
Architectural bias in recurrent neural networks – fractal analysis.
In: Proc. International Conf. on Artificial Neural Networks. Lecture Notes in Computer Science, 2415. Dorronsoro JR (Ed); Berlin: Springer: 370-376.
PUB
Architectural bias in recurrent neural networks – fractal analysis.
In: Proc. International Conf. on Artificial Neural Networks. Lecture Notes in Computer Science, 2415. Dorronsoro JR (Ed); Berlin: Springer: 370-376.
2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994146
Villmann T, Hammer B (2002)
Supervised Neural Gas for Learning Vector Quantization.
In: Proc. of the 5th German Workshop on Artificial Life. Polani D, Kim J, Martinetz T (Eds); Berlin: Akademische Verlagsgesellschaft - infix - IOS Press: 9-16.
PUB
Supervised Neural Gas for Learning Vector Quantization.
In: Proc. of the 5th German Workshop on Artificial Life. Polani D, Kim J, Martinetz T (Eds); Berlin: Akademische Verlagsgesellschaft - infix - IOS Press: 9-16.
2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993688
Hammer B, Steil JJ (2002)
Perspectives on Learning with Recurrent Neural Networks.
In: Proc. European Symposium Artificial Neural Networks. Verleysen M (Ed); D-side publication: 357-368.
PUB
Perspectives on Learning with Recurrent Neural Networks.
In: Proc. European Symposium Artificial Neural Networks. Verleysen M (Ed); D-side publication: 357-368.
2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993758
Hammer B, Villmann T (2002)
Batch-GRLVQ.
In: Proc. Of European Symposium on Artificial Neural Networks (ESANN'2002). Verleysen M (Ed); Brussels, Belgium: d-side: 295-300.
PUB
Batch-GRLVQ.
In: Proc. Of European Symposium on Artificial Neural Networks (ESANN'2002). Verleysen M (Ed); Brussels, Belgium: d-side: 295-300.
2002 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993765
Hammer B, Villmann T (2002)
Generalized Relevance Learning Vector Quantization.
Neural Networks 15(8-9): 1059-1068.
PUB
Generalized Relevance Learning Vector Quantization.
Neural Networks 15(8-9): 1059-1068.
2002 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1993471
Hammer B (2002)
Compositionality in Neural Systems.
In: Handbook of Brain Theory and Neural Networks. Arbib M (Ed); 2nd. MIT Press: 244-248.
PUB
Compositionality in Neural Systems.
In: Handbook of Brain Theory and Neural Networks. Arbib M (Ed); 2nd. MIT Press: 244-248.
2002 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993508
Hammer B (2002)
Recurrent neural networks for structured data – a unifying approach and its properties.
Cognitive Systems Research 3(2): 145-165.
PUB
Recurrent neural networks for structured data – a unifying approach and its properties.
Cognitive Systems Research 3(2): 145-165.
2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993692
Hammer B, Strickert M, Villmann T (2002)
Learning Vector Quantization for Multimodal Data.
In: Proc. International Conf. on Artificial Neural Networks (ICANN). Lecture Notes in Computer Science, 2415. Dorronsoro JR (Ed); Berlin: Springer Verlag: 370-376.
PUB
Learning Vector Quantization for Multimodal Data.
In: Proc. International Conf. on Artificial Neural Networks (ICANN). Lecture Notes in Computer Science, 2415. Dorronsoro JR (Ed); Berlin: Springer Verlag: 370-376.
2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993697
Hammer B, Strickert M, Villmann T (2002)
Rule Extraction from Self-Organizing Networks.
In: Proc. International Conf. on Artificial Neural Networks (ICANN). Lecture Notes in Computer Science, 2415. Dorronsoro JR (Ed); Berlin: Springer Verlag: 877-883.
PUB
Rule Extraction from Self-Organizing Networks.
In: Proc. International Conf. on Artificial Neural Networks (ICANN). Lecture Notes in Computer Science, 2415. Dorronsoro JR (Ed); Berlin: Springer Verlag: 877-883.
2002 | Report | Veröffentlicht | PUB-ID: 1993729
Hammer B, Tino P (2002)
Neural networks with small weights implement finite memory machines. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
PUB
Neural networks with small weights implement finite memory machines. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
2001 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993768
Hammer B, Villmann T (2001)
Input Pruning for Neural Gas Architectures.
In: Proc. Of European Symposium on Artificial Neural Networks (ESANN'2001). Brussels, Belgium: D facto publications: 283-288.
PUB
Input Pruning for Neural Gas Architectures.
In: Proc. Of European Symposium on Artificial Neural Networks (ESANN'2001). Brussels, Belgium: D facto publications: 283-288.
2001 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993343
Bojer T, Hammer B, Schunk D, Tluk von Toschanowitz K (2001)
Relevance determination in learning vector quantization.
In: ESANN'2001. Verleysen M (Ed); D-facto publications: 271-276.
PUB
Relevance determination in learning vector quantization.
In: ESANN'2001. Verleysen M (Ed); D-facto publications: 271-276.
2001 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994123
Vidyasagar M, Balaji S, Hammer B (2001)
Closure properties of uniform convergence of empirical means and PAC learnability under a family of probability measures.
System and Control Letters 42: 151-157.
PUB
Closure properties of uniform convergence of empirical means and PAC learnability under a family of probability measures.
System and Control Letters 42: 151-157.
2001 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993474
Hammer B (2001)
On the Generalization Ability of Recurrent Networks.
In: Artificial Neural Networks. Proceedings. Lecture Notes in Computer Science, 2130. Dorffner G, Bischof H, Hornik K (Eds); Berlin: Springer: 731-736.
PUB
On the Generalization Ability of Recurrent Networks.
In: Artificial Neural Networks. Proceedings. Lecture Notes in Computer Science, 2130. Dorffner G, Bischof H, Hornik K (Eds); Berlin: Springer: 731-736.
2001 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993739
Hammer B, Villmann T (2001)
Estimating Relevant Input Dimensions for Self-Organizing Algorithms.
In: Advances in Self-Organising Maps. Allinson NM, Yin H, Allinson L, Slack J (Eds); London: Springer: 173-180.
PUB
Estimating Relevant Input Dimensions for Self-Organizing Algorithms.
In: Advances in Self-Organising Maps. Allinson NM, Yin H, Allinson L, Slack J (Eds); London: Springer: 173-180.
2001 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994042
Strickert M, Bojer T, Hammer B (2001)
Generalized Relevance LVQ for Time Series.
In: Artificial Neural Networks. International Conference. Proceedings. Lecture Notes in Computer Science, 2130. Dorffner G, Bischof H, Hornik K (Eds); Berlin: Springer: 677-683.
PUB
Generalized Relevance LVQ for Time Series.
In: Artificial Neural Networks. International Conference. Proceedings. Lecture Notes in Computer Science, 2130. Dorffner G, Bischof H, Hornik K (Eds); Berlin: Springer: 677-683.
2001 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993510
Hammer B (2001)
Generalization Ability of Folding Networks.
IEEE Trans. Knowl. Data Eng. 13(2): 196-206.
PUB
Generalization Ability of Folding Networks.
IEEE Trans. Knowl. Data Eng. 13(2): 196-206.
2000 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993499
Hammer B (2000)
Limitations of hybrid systems.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); D-facto publications: 213-218.
PUB
Limitations of hybrid systems.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); D-facto publications: 213-218.
2000 | Monographie | Veröffentlicht | PUB-ID: 1993514
Hammer B (2000)
Learning with Recurrent Neural Networks. Lecture Notes in Control and Information Sciences, 254.
Berlin: Springer.
PUB
Learning with Recurrent Neural Networks. Lecture Notes in Control and Information Sciences, 254.
Berlin: Springer.
2000 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993512
Hammer B (2000)
On the approximation capability of recurrent neural networks.
Neurocomputing 31(1-4): 107-123.
PUB
On the approximation capability of recurrent neural networks.
Neurocomputing 31(1-4): 107-123.
2000 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993400
DasGupta B, Hammer B (2000)
On Approximate Learning by Multi-layered Feedforward Circuits.
In: Algorithmic Learning Theory, 11th International Conference. Proceedings. Lecture Notes in Computer Science, 1968. Arimura H, Jain S, Sharma A (Eds); Berlin: Springer: 264-278.
PUB
On Approximate Learning by Multi-layered Feedforward Circuits.
In: Algorithmic Learning Theory, 11th International Conference. Proceedings. Lecture Notes in Computer Science, 1968. Arimura H, Jain S, Sharma A (Eds); Berlin: Springer: 264-278.
2000 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993479
Hammer B (2000)
Approximation and generalization issues of recurrent networks dealing with structured data.
In: ECAI workshop: Foundations of connectionist-symbolic integration: representation, paradigms, and algorithms. Frasconi P, Sperduti A, Gori M (Eds);.
PUB
Approximation and generalization issues of recurrent networks dealing with structured data.
In: ECAI workshop: Foundations of connectionist-symbolic integration: representation, paradigms, and algorithms. Frasconi P, Sperduti A, Gori M (Eds);.
2000 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993495
Hammer B (2000)
Neural networks classifying symbolic data.
In: ICML workshop on attribute-value and relational learning: crossing the boundaries. de Raedt L, Kramer S (Eds); 61-65.
PUB
Neural networks classifying symbolic data.
In: ICML workshop on attribute-value and relational learning: crossing the boundaries. de Raedt L, Kramer S (Eds); 61-65.
1999 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993516
Hammer B (1999)
On the learnability of recursive data.
Mathematics of Control, Signals and Systems 12: 62-79.
PUB
On the learnability of recursive data.
Mathematics of Control, Signals and Systems 12: 62-79.
1999 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993502
Hammer B (1999)
Approximation capabilities of folding networks.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); D-facto publications: 33-38.
PUB
Approximation capabilities of folding networks.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); D-facto publications: 33-38.
1999 | Report | Veröffentlicht | PUB-ID: 1993409
DasGupta B, Hammer B (1999)
Hardness of approximation of the loading problem for multi-layered feedforward neural networks.
DIMACS Center, Rutgers University.
PUB
Hardness of approximation of the loading problem for multi-layered feedforward neural networks.
DIMACS Center, Rutgers University.
1998 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993484
Hammer B (1998)
On the Approximation Capability of Recurrent Neural Networks.
In: Proceedings of the International ICSC / IFAC Symposium on Neural Computation (NC 1998). Heiss M (Ed); ICSC Academic Press: 512-518.
PUB
On the Approximation Capability of Recurrent Neural Networks.
In: Proceedings of the International ICSC / IFAC Symposium on Neural Computation (NC 1998). Heiss M (Ed); ICSC Academic Press: 512-518.
1998 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993505
Hammer B (1998)
Training a sigmoidal network is difficult.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); D-facto publications: 255-260.
PUB
Training a sigmoidal network is difficult.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); D-facto publications: 255-260.
1998 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993518
Hammer B (1998)
Some complexity results for perceptron networks.
In: International Conference on artificial Neural Networks. 639-644.
PUB
Some complexity results for perceptron networks.
In: International Conference on artificial Neural Networks. 639-644.
1997 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993526
Hammer B (1997)
Generalization of Elman Networks.
In: Artificial Neural Networks - ICANN '97, 7th International Conference. Proceedings. Lecture Notes in Computer Science, 1327. Berlin: Springer: 409-414.
PUB
Generalization of Elman Networks.
In: Artificial Neural Networks - ICANN '97, 7th International Conference. Proceedings. Lecture Notes in Computer Science, 1327. Berlin: Springer: 409-414.
1997 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993684
Hammer B, Sperschneider V (1997)
Neural networks can approximate mappings on structured objects.
In: International conference on Computational Intelligence and Neural Networks. Wang PP (Ed); 211-214.
PUB
Neural networks can approximate mappings on structured objects.
In: International conference on Computational Intelligence and Neural Networks. Wang PP (Ed); 211-214.
1997 | Report | Veröffentlicht | PUB-ID: 1993524
Hammer B (1997)
On the generalization ability of simple recurrent neural networks. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
PUB
On the generalization ability of simple recurrent neural networks. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
1997 | Report | Veröffentlicht | PUB-ID: 1993520
Hammer B (1997)
Learning recursive data is intractable. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
PUB
Learning recursive data is intractable. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
1997 | Report | Veröffentlicht | PUB-ID: 1993522
Hammer B (1997)
A NP-hardness result for a sigmoidal 3-node neural network. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
PUB
A NP-hardness result for a sigmoidal 3-node neural network. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
1996 | Report | Veröffentlicht | PUB-ID: 1993528
Hammer B (1996)
Universal approximation of mappings on structured objects using the folding architecture. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
PUB
Universal approximation of mappings on structured objects using the folding architecture. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
1996 | Monographie | Veröffentlicht | PUB-ID: 1994039
Sperschneider V, Hammer B (1996)
Theoretische Informatik. Eine problemorientierte Einführung.
erlin: Springer.
PUB
Theoretische Informatik. Eine problemorientierte Einführung.
erlin: Springer.
Suche
Publikationen filtern
Darstellung / Sortierung
Export / Einbettung
392 Publikationen
2021 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2957373
Artelt A, Hinder F, Vaquet V, Feldhans R, Hammer B (2021)
Contrastive Explanations for Explaining Model Adaptations.
In: Advances in Computational Intelligence. 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Virtual Event, June 16–18, 2021, Proceedings, Part I. Rojas I, Joya G, Catala A (Eds); Lecture Notes in Computer Science, Cham: Springer International Publishing: 101-112.
PUB | DOI
Contrastive Explanations for Explaining Model Adaptations.
In: Advances in Computational Intelligence. 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Virtual Event, June 16–18, 2021, Proceedings, Part I. Rojas I, Joya G, Catala A (Eds); Lecture Notes in Computer Science, Cham: Springer International Publishing: 101-112.
2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2949334
Rohlfing K, Cimiano P, Scharlau I, Matzner T, Buhl HM, Buschmeier H, Esposito E, Grimminger A, Hammer B, Häb-Umbach R, Horwath I, Hüllermeier E, Kern F, Kopp S, Thommes K, Ngonga Ngomo A-C, Schulte C, Wachsmuth H, Wagner P, Wrede B (2021)
Explanation as a social practice: Toward a conceptual framework for the social design of AI systems.
IEEE Transactions on Cognitive and Developmental Systems 13(3): 717--728.
PUB
| PDF | DOI
Explanation as a social practice: Toward a conceptual framework for the social design of AI systems.
IEEE Transactions on Cognitive and Developmental Systems 13(3): 717--728.
2021 | Zeitschriftenaufsatz | Im Druck | PUB-ID: 2957340
Artelt A, Hammer B (In Press)
Efficient computation of counterfactual explanations and counterfactual metrics of prototype-based classifiers.
Neurocomputing VSI: ESANN 2020.
PUB | DOI | Download (ext.)
Efficient computation of counterfactual explanations and counterfactual metrics of prototype-based classifiers.
Neurocomputing VSI: ESANN 2020.
2021 | Konferenzbeitrag | Angenommen | PUB-ID: 2956774
Hinder F, Hammer B (Accepted)
Concept Drift Segmentation via Kolmogorov Trees.
In: Proceedings of the ESANN, 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed);.
PUB
Concept Drift Segmentation via Kolmogorov Trees.
In: Proceedings of the ESANN, 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed);.
2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2956229
Paassen B, Schulz A, Stewart TC, Hammer B (2021)
Reservoir Memory Machines as Neural Computers.
IEEE Transactions on Neural Networks and Learning Systems: 1-11.
PUB | DOI | Download (ext.) | arXiv
Reservoir Memory Machines as Neural Computers.
IEEE Transactions on Neural Networks and Learning Systems: 1-11.
2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2954542
Paaßen B, Schulz A, Hammer B (2021)
Reservoir Stack Machines.
Neurocomputing.
PUB | DOI | Download (ext.) | arXiv
Reservoir Stack Machines.
Neurocomputing.
2021 | Konferenzbeitrag | Angenommen | PUB-ID: 2955948
Brinkrolf J, Hammer B (Accepted)
Federated Learning Vector Quantization.
In: Proceedings of the ESANN, 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed);.
PUB
Federated Learning Vector Quantization.
In: Proceedings of the ESANN, 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed);.
2021 | Zeitschriftenaufsatz | Angenommen | PUB-ID: 2955245
Stallmann D, Göpfert JP, Schmitz J, Grünberger A, Hammer B (Accepted)
Towards an automatic analysis of CHO-K1 suspension growth in microfluidic single-cell cultivation.
Bioinformatics .
PUB | DOI | PubMed | Europe PMC
Towards an automatic analysis of CHO-K1 suspension growth in microfluidic single-cell cultivation.
Bioinformatics .
2021 | Report | PUB-ID: 2954239
Szczuka J, Artelt A, Geminn C, Hammer B, Kopp S, Manzeschke A, Rossnagel A, Slawik P, Strathmann C, Szymczyk N, Varonina L, Weber C (2021)
Können Kinder aufgeklärte Nutzer* innen von Sprachassistenten sein? Rechtliche, psychologische, ethische und informatische Perspektiven.
Universität Duisburg-Essen, Universitätsbibliothek.
PUB | DOI
Können Kinder aufgeklärte Nutzer* innen von Sprachassistenten sein? Rechtliche, psychologische, ethische und informatische Perspektiven.
Universität Duisburg-Essen, Universitätsbibliothek.
2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2940666
Brinkrolf J, Hammer B (2020)
Sparse Metric Learning in Prototype-based Classification.
In: Proceedings of the ESANN, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); 375-380.
PUB
Sparse Metric Learning in Prototype-based Classification.
In: Proceedings of the ESANN, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); 375-380.
2020 | Konferenzbeitrag | PUB-ID: 2943260
Schulz A, Hinder F, Hammer B (2020)
DeepView: Visualizing Classification Boundaries of Deep Neural Networks as Scatter Plots Using Discriminative Dimensionality Reduction.
In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, {IJCAI-20}. .
PUB | DOI | Download (ext.)
DeepView: Visualizing Classification Boundaries of Deep Neural Networks as Scatter Plots Using Discriminative Dimensionality Reduction.
In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, {IJCAI-20}. .
2020 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2939517
Pfannschmidt L, Jakob J, Hinder F, Biehl M, Tino P, Hammer B (2020)
Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information.
Neurocomputing.
PUB | DOI | Download (ext.) | WoS | arXiv
Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information.
Neurocomputing.
2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2946761
Artelt A, Hammer B (2020)
Convex Density Constraints for Computing Plausible Counterfactual Explanations.
In: Artificial Neural Networks and Machine Learning - ICANN 2020 - 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15-18, 2020, Proceedings, Part {I}. Farkas I, Masulli P, Wermter S (Eds); Lecture Notes in Computer Science, 12396. Cham: Springer: 353-365.
PUB | DOI | Download (ext.)
Convex Density Constraints for Computing Plausible Counterfactual Explanations.
In: Artificial Neural Networks and Machine Learning - ICANN 2020 - 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15-18, 2020, Proceedings, Part {I}. Farkas I, Masulli P, Wermter S (Eds); Lecture Notes in Computer Science, 12396. Cham: Springer: 353-365.
2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2946685
Artelt A, Hammer B (2020)
Efficient computation of counterfactual explanations of LVQ models.
In: ESANN 2020 - proceedings. Verleysen M (Ed); Louvain-la-Neuve: Ciaco : 19-24.
PUB | Download (ext.)
Efficient computation of counterfactual explanations of LVQ models.
In: ESANN 2020 - proceedings. Verleysen M (Ed); Louvain-la-Neuve: Ciaco : 19-24.
2020 | Konferenzbeitrag | PUB-ID: 2946488
Hinder F, Artelt A, Hammer B (2020)
Towards non-parametric drift detection via Dynamic Adapting Window Independence Drift Detection (DAWIDD).
In: Proceedings of the 37th International Conference on Machine Learning. .
PUB | Download (ext.)
Towards non-parametric drift detection via Dynamic Adapting Window Independence Drift Detection (DAWIDD).
In: Proceedings of the 37th International Conference on Machine Learning. .
2019 | Monographie | PUB-ID: 2935200
Paaßen B, Artelt A, Hammer B (2019)
Lecture Notes on Applied Optimization.
Faculty of Technology, Bielefeld University.
PUB
| Dateien verfügbar
Lecture Notes on Applied Optimization.
Faculty of Technology, Bielefeld University.
2019 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2934458
Prahm C, Schulz A, Paaßen B, Schoisswohl J, Kaniusas E, Dorffner G, Hammer B, Aszmann O (2019)
Counteracting Electrode Shifts in Upper-Limb Prosthesis Control via Transfer Learning.
IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(5): 956-962.
PUB
| PDF | DOI | WoS | PubMed | Europe PMC
Counteracting Electrode Shifts in Upper-Limb Prosthesis Control via Transfer Learning.
IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(5): 956-962.
2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2933893
Pfannschmidt L, Jakob J, Biehl M, Tino P, Hammer B (2019)
Feature Relevance Bounds for Ordinal Regression.
In: Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019). Verleysen M (Ed); Louvain-la-Neuve: i6doc.
PUB | Download (ext.) | arXiv
Feature Relevance Bounds for Ordinal Regression.
In: Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019). Verleysen M (Ed); Louvain-la-Neuve: i6doc.
2019 | Konferenzbeitrag | Angenommen | PUB-ID: 2937841
Hosseini B, Hammer B (Accepted)
Interpretable Multiple-Kernel Prototype Learning for Discriminative Representation and Feature Selection.
Presented at the The 28th ACM International Conference on Information and Knowledge Management (CIKM) , Beijing.
PUB
| Datei | arXiv
Interpretable Multiple-Kernel Prototype Learning for Discriminative Representation and Feature Selection.
Presented at the The 28th ACM International Conference on Information and Knowledge Management (CIKM) , Beijing.
2019 | Report | Veröffentlicht | PUB-ID: 2937888
Krämer N, Artelt A, Geminn C, Hammer B, Kopp S, Manzeschke A, Rossnagel A, Slawik P, Szczuka J, Varonina L, Weber C (2019)
KI-basierte Sprachassistenten im Alltag: Forschungsbedarf aus informatischer, psychologischer, ethischer und rechtlicher Sicht.
Universität Duisburg-Essen, Universitätsbibliothek.
PUB | DOI | Download (ext.)
KI-basierte Sprachassistenten im Alltag: Forschungsbedarf aus informatischer, psychologischer, ethischer und rechtlicher Sicht.
Universität Duisburg-Essen, Universitätsbibliothek.
2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2937839
Hosseini B, Hammer B (2019)
Interpretable Discriminative Dimensionality Reduction and Feature Selection on the Manifold.
Presented at the 2019 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML), Würzburg.
PUB
| Datei | arXiv
Interpretable Discriminative Dimensionality Reduction and Feature Selection on the Manifold.
Presented at the 2019 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML), Würzburg.
2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2935456
Pfannschmidt L, Göpfert C, Neumann U, Heider D, Hammer B (2019)
FRI - Feature Relevance Intervals for Interpretable and Interactive Data Exploration.
Presented at the 16th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, Certosa di Pontignano, Siena - Tuscany, Italy.
PUB
| PDF | DOI | arXiv
FRI - Feature Relevance Intervals for Interpretable and Interactive Data Exploration.
Presented at the 16th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, Certosa di Pontignano, Siena - Tuscany, Italy.
2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2932412
Straat M, Abadi F, Göpfert C, Hammer B, Biehl M (2018)
Statistical Mechanics of On-Line Learning Under Concept Drift.
ENTROPY 20(10): 775.
PUB | DOI | WoS | PubMed | Europe PMC
Statistical Mechanics of On-Line Learning Under Concept Drift.
ENTROPY 20(10): 775.
2018 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2917896
Lux M, Brinkman RR, Chauve C, Laing A, Lorenc A, Abeler-Dörner L, Hammer B (2018)
flowLearn: Fast and precise identification and quality checking of cell populations in flow cytometry.
Bioinformatics 34(13): 2245-2253.
PUB | DOI | WoS | PubMed | Europe PMC
flowLearn: Fast and precise identification and quality checking of cell populations in flow cytometry.
Bioinformatics 34(13): 2245-2253.
2018 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2933557
Meyer S, Bertrand O, Egelhaaf M, Hammer B (2018)
Inferring Temporal Structure from Predictability in Bumblebee Learning Flight.
In: Intelligent Data Engineering and Automated Learning – IDEAL 2018. Yin H, Camacho D, Novais P, Tallón-Ballesteros AJ (Eds); Lecture Notes in Computer Science, 11314. Cham: Springer International Publishing: 508-519.
PUB | DOI
Inferring Temporal Structure from Predictability in Bumblebee Learning Flight.
In: Intelligent Data Engineering and Automated Learning – IDEAL 2018. Yin H, Camacho D, Novais P, Tallón-Ballesteros AJ (Eds); Lecture Notes in Computer Science, 11314. Cham: Springer International Publishing: 508-519.
2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2918254
Brinkrolf J, Berger K, Hammer B (2018)
Differential private relevance learning.
In: Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018). Verleysen M (Ed); 555-560.
PUB | Download (ext.)
Differential private relevance learning.
In: Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018). Verleysen M (Ed); 555-560.
2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2911900
Paaßen B, Göpfert C, Hammer B (2018)
Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces.
Neural Processing Letters 48(2): 669-689.
PUB | DOI | Download (ext.) | WoS | arXiv
Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces.
Neural Processing Letters 48(2): 669-689.
2018 | Preprint | Veröffentlicht | PUB-ID: 2921209
Hosseini B, Hammer B (2018)
Non-Negative Local Sparse Coding for Subspace Clustering.
Advances in Intelligent Data Analysis XVII. IDA 2018.
PUB
| Datei | Download (ext.) | arXiv
Non-Negative Local Sparse Coding for Subspace Clustering.
Advances in Intelligent Data Analysis XVII. IDA 2018.
2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2919598
Hosseini B, Hammer B (2018)
Feasibility Based Large Margin Nearest Neighbor Metric Learning.
In: ESANN 2018. Proceedings of 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 219-224.
PUB | arXiv
Feasibility Based Large Margin Nearest Neighbor Metric Learning.
In: ESANN 2018. Proceedings of 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 219-224.
2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2914505
Paaßen B, Schulz A, Hahne J, Hammer B (2018)
Expectation maximization transfer learning and its application for bionic hand prostheses.
Neurocomputing 298: 122-133.
PUB | DOI | Download (ext.) | WoS | arXiv
Expectation maximization transfer learning and its application for bionic hand prostheses.
Neurocomputing 298: 122-133.
2018 | Datenpublikation | PUB-ID: 2930611
Hülsmann F, Göpfert JP, Hammer B, Kopp S, Botsch M (2018)
Classification of motor errors to provide real-time feedback for sports coaching in virtual reality - A case study in squats and Tai Chi pushes (Data).
Bielefeld University.
PUB
| Dateien verfügbar | DOI
Classification of motor errors to provide real-time feedback for sports coaching in virtual reality - A case study in squats and Tai Chi pushes (Data).
Bielefeld University.
2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2921316
Göpfert JP, Hammer B, Wersing H (2018)
Mitigating Concept Drift via Rejection.
In: Artificial Neural Networks and Machine Learning – ICANN 2018. Proceedings, Part I. Kurkova V, Manolopoulos Y, Hammer B, Iliadis L, Maglogiannis I (Eds); Lecture Notes in Computer Science, 11139. Cham: Springer.
PUB
| PDF | DOI
Mitigating Concept Drift via Rejection.
In: Artificial Neural Networks and Machine Learning – ICANN 2018. Proceedings, Part I. Kurkova V, Manolopoulos Y, Hammer B, Iliadis L, Maglogiannis I (Eds); Lecture Notes in Computer Science, 11139. Cham: Springer.
2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2930862
Hülsmann F, Göpfert JP, Hammer B, Kopp S, Botsch M (2018)
Classification of motor errors to provide real-time feedback for sports coaching in virtual reality — A case study in squats and Tai Chi pushes.
Computers & Graphics 76: 47-59.
PUB | DOI | Download (ext.) | WoS
Classification of motor errors to provide real-time feedback for sports coaching in virtual reality — A case study in squats and Tai Chi pushes.
Computers & Graphics 76: 47-59.
2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2931283
Queißer J, Ishihara H, Hammer B, Steil JJ, Asada M (2018)
Skill Memories for Parameterized Dynamic Action Primitives on the Pneumatically Driven Humanoid Robot Child Affetto.
Presented at the International Conference on Development and Learning and on Epigenetic Robotics 2018 (ICDL-EPIROB2018), Tokyo .
PUB
| PDF
Skill Memories for Parameterized Dynamic Action Primitives on the Pneumatically Driven Humanoid Robot Child Affetto.
Presented at the International Conference on Development and Learning and on Epigenetic Robotics 2018 (ICDL-EPIROB2018), Tokyo .
2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2913389
Paaßen B, Hammer B, Price T, Barnes T, Gross S, Pinkwart N (2018)
The Continuous Hint Factory - Providing Hints in Vast and Sparsely Populated Edit Distance Spaces.
Journal of Educational Data Mining 10(1): 1-35.
PUB | Download (ext.) | arXiv
The Continuous Hint Factory - Providing Hints in Vast and Sparsely Populated Edit Distance Spaces.
Journal of Educational Data Mining 10(1): 1-35.
2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2919844
Paaßen B, Gallicchio C, Micheli A, Hammer B (2018)
Tree Edit Distance Learning via Adaptive Symbol Embeddings.
In: Proceedings of the 35th International Conference on Machine Learning (ICML 2018). Dy J, Krause A (Eds); Proceedings of Machine Learning Research, 80. 3973-3982.
PUB | Download (ext.) | arXiv
Tree Edit Distance Learning via Adaptive Symbol Embeddings.
In: Proceedings of the 35th International Conference on Machine Learning (ICML 2018). Dy J, Krause A (Eds); Proceedings of Machine Learning Research, 80. 3973-3982.
2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909369
Paaßen B, Schulz A, Hahne J, Hammer B (2017)
An EM transfer learning algorithm with applications in bionic hand prostheses.
In: Proceedings of the 25th European Symposium on Artificial Neural Networks (ESANN 2017). Verleysen M (Ed); Bruges: i6doc.com: 129-134.
PUB
| PDF
An EM transfer learning algorithm with applications in bionic hand prostheses.
In: Proceedings of the 25th European Symposium on Artificial Neural Networks (ESANN 2017). Verleysen M (Ed); Bruges: i6doc.com: 129-134.
2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2914945
Brinkrolf J, Hammer B (2017)
Probabilistic extension and reject options for pairwise LVQ.
In: 2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM). Piscataway, NJ: IEEE.
PUB | DOI
Probabilistic extension and reject options for pairwise LVQ.
In: 2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM). Piscataway, NJ: IEEE.
2017 | Konferenzbeitrag | PUB-ID: 2909371
Biehl M, Hammer B, Villmann T (2017)
Prototype based models for the supervised learning of classificaton schemes.
In: Proc. of the IAU Symposium 325 on Astroinformatics, Sorrento/Italy, October 2016. in press.
PUB
Prototype based models for the supervised learning of classificaton schemes.
In: Proc. of the IAU Symposium 325 on Astroinformatics, Sorrento/Italy, October 2016. in press.
2017 | Konferenzbeitrag | PUB-ID: 2914950
Brinkrolf J, Berger K, Hammer B (2017)
Differential Privacy for Learning Vector Quantization.
In: New Challenges in Neural Computation. .
PUB
Differential Privacy for Learning Vector Quantization.
In: New Challenges in Neural Computation. .
2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2908201
Göpfert C, Pfannschmidt L, Hammer B (2017)
Feature Relevance Bounds for Linear Classification.
In: Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Louvain-la-Neuve: Ciaco - i6doc.com: 187--192.
PUB
| Dateien verfügbar | Download (ext.)
Feature Relevance Bounds for Linear Classification.
In: Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Louvain-la-Neuve: Ciaco - i6doc.com: 187--192.
2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2913752
Göpfert JP, Göpfert C, Botsch M, Hammer B (2017)
Effects of Variability in Synthetic Training Data on Convolutional Neural Networks for 3D Head Reconstruction.
In: 2017 SSCI Proceedings. 2017 IEEE Symposium Series on Computational Intelligence (SSCI). Piscataway, NJ: IEEE.
PUB
| PDF | DOI
Effects of Variability in Synthetic Training Data on Convolutional Neural Networks for 3D Head Reconstruction.
In: 2017 SSCI Proceedings. 2017 IEEE Symposium Series on Computational Intelligence (SSCI). Piscataway, NJ: IEEE.
2017 | Konferenzbeitrag | PUB-ID: 2909370
Frenay B, Hammer B (2017)
Label-Noise-Tolerant Classification for Streaming Data.
In: IEEE International Joint Conference on Neural Neworks. .
PUB
Label-Noise-Tolerant Classification for Streaming Data.
In: IEEE International Joint Conference on Neural Neworks. .
2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2914141
Aswolinskiy W, Hammer B (2017)
Unsupervised Transfer Learning for Time Series via Self-Predictive Modelling - First Results.
In: Proceedings of the Workshop on New Challenges in Neural Computation (NC2). Machine Learning Reports, 03/2017. Bielefeld: Universität Bielefeld, CITEC.
PUB
| PDF
Unsupervised Transfer Learning for Time Series via Self-Predictive Modelling - First Results.
In: Proceedings of the Workshop on New Challenges in Neural Computation (NC2). Machine Learning Reports, 03/2017. Bielefeld: Universität Bielefeld, CITEC.
2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909037
Prahm C, Schulz A, Paaßen B, Aszmann O, Hammer B, Dorffner G (2017)
Echo State Networks as Novel Approach for Low-Cost Myoelectric Control.
In: Proceedings of the 16th Conference on Artificial Intelligence in Medicine (AIME 2017). ten Telje A, Popow C, Holmes JH, Sacchi L (Eds); Lecture Notes in Computer Science, 10259. Springer: 338--342.
PUB
| Dateien verfügbar | DOI
Echo State Networks as Novel Approach for Low-Cost Myoelectric Control.
In: Proceedings of the 16th Conference on Artificial Intelligence in Medicine (AIME 2017). ten Telje A, Popow C, Holmes JH, Sacchi L (Eds); Lecture Notes in Computer Science, 10259. Springer: 338--342.
2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2915274
Göpfert C, Göpfert JP, Hammer B (2017)
Analyzing Feature Relevance for Linear Reject Option SVM using Relevance Intervals.
In: Proceedings of the 2017 NIPS workshop on Transparent and Interpretable Machine Learning in Safety Critical Environments. .
PUB
| PDF
Analyzing Feature Relevance for Linear Reject Option SVM using Relevance Intervals.
In: Proceedings of the 2017 NIPS workshop on Transparent and Interpretable Machine Learning in Safety Critical Environments. .
2016 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2907633
Lux M, Krüger J, Rinke C, Maus I, Schlüter A, Woyke T, Sczyrba A, Hammer B (2016)
acdc – Automated Contamination Detection and Confidence estimation for single-cell genome data.
BMC Bioinformatics 17(1): 543.
PUB
| PDF | DOI | WoS | PubMed | Europe PMC
acdc – Automated Contamination Detection and Confidence estimation for single-cell genome data.
BMC Bioinformatics 17(1): 543.
2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909367
Kummert J, Paaßen B, Jensen J, Göpfert C, Hammer B (2016)
Local Reject Option for Deterministic Multi-class SVM.
In: Artificial Neural Networks and Machine Learning - ICANN 2016 - 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II. E.P. Villa A, Masulli P, Pons Rivero AJ (Eds); Lecture Notes in Computer Science, 9887. Cham: Springer Nature: 251--258.
PUB | DOI
Local Reject Option for Deterministic Multi-class SVM.
In: Artificial Neural Networks and Machine Learning - ICANN 2016 - 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II. E.P. Villa A, Masulli P, Pons Rivero AJ (Eds); Lecture Notes in Computer Science, 9887. Cham: Springer Nature: 251--258.
2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2904469
Hosseini B, Hülsmann F, Botsch M, Hammer B (2016)
Non-Negative Kernel Sparse Coding for the Analysis of Motion Data.
In: Artificial Neural Networks and Machine Learning – ICANN 2016. E.P. Villa A, Masulli P, Javier Pons Rivero A (Eds); Lecture Notes in Computer Science, 9887. Cham: Springer: 506-514.
PUB
| PDF | DOI | Download (ext.) | arXiv
Non-Negative Kernel Sparse Coding for the Analysis of Motion Data.
In: Artificial Neural Networks and Machine Learning – ICANN 2016. E.P. Villa A, Masulli P, Javier Pons Rivero A (Eds); Lecture Notes in Computer Science, 9887. Cham: Springer: 506-514.
2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900676
Paaßen B, Göpfert C, Hammer B (2016)
Gaussian process prediction for time series of structured data.
In: Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Louvain-la-Neuve: Ciaco - i6doc.com: 41--46.
PUB
| PDF
Gaussian process prediction for time series of structured data.
In: Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Louvain-la-Neuve: Ciaco - i6doc.com: 41--46.
2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2904509
Paaßen B, Jensen J, Hammer B (2016)
Execution Traces as a Powerful Data Representation for Intelligent Tutoring Systems for Programming.
In: Proceedings of the 9th International Conference on Educational Data Mining. Barnes T, Chi M, Feng M (Eds); Raleigh, North Carolina, USA: International Educational Datamining Society: 183-190.
PUB | Download (ext.)
Execution Traces as a Powerful Data Representation for Intelligent Tutoring Systems for Programming.
In: Proceedings of the 9th International Conference on Educational Data Mining. Barnes T, Chi M, Feng M (Eds); Raleigh, North Carolina, USA: International Educational Datamining Society: 183-190.
2016 | Konferenzbeitrag | E-Veröff. vor dem Druck | PUB-ID: 2904909
Schulz A, Hammer B (2016)
Discriminative Dimensionality Reduction in Kernel Space.
In: ESANN2016 Proceedings. 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges, Belgium,27-29 April 2016. i6doc.com.
PUB
| PDF
Discriminative Dimensionality Reduction in Kernel Space.
In: ESANN2016 Proceedings. 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges, Belgium,27-29 April 2016. i6doc.com.
2016 | Konferenzbeitrag | PUB-ID: 2909365
Brinkrolf J, Mittag T, Joppen R, Dr\ A, Pietsch K-H, Hammer B (2016)
Virtual optimisation for improved production planning.
In: New Challenges in Neural Computation. .
PUB
Virtual optimisation for improved production planning.
In: New Challenges in Neural Computation. .
2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2905729
Göpfert C, Paaßen B, Hammer B (2016)
Convergence of Multi-pass Large Margin Nearest Neighbor Metric Learning.
In: Artificial Neural Networks and Machine Learning – ICANN 2016: 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II. E.P. Villa A, Masulli P, Pons Rivero AJ (Eds); Lecture Notes in Computer Science, 9887. Cham: Springer Nature: 510-517.
PUB
| PDF | DOI
Convergence of Multi-pass Large Margin Nearest Neighbor Metric Learning.
In: Artificial Neural Networks and Machine Learning – ICANN 2016: 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II. E.P. Villa A, Masulli P, Pons Rivero AJ (Eds); Lecture Notes in Computer Science, 9887. Cham: Springer Nature: 510-517.
2016 | Konferenzbeitrag | PUB-ID: 2908455
Losing V, Hammer B, Wersing H (2016)
Dedicated Memory Models for Continual Learning in the Presence of Concept Drift.
Presented at the Continual Learning Workshop of the Thirtieth Annual Conference on Neural Information Processing Systems (NIPS), Barcelona.
PUB
| PDF
Dedicated Memory Models for Continual Learning in the Presence of Concept Drift.
Presented at the Continual Learning Workshop of the Thirtieth Annual Conference on Neural Information Processing Systems (NIPS), Barcelona.
2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2905855
Paaßen B, Schulz A, Hammer B (2016)
Linear Supervised Transfer Learning for Generalized Matrix LVQ.
In: Proceedings of the Workshop New Challenges in Neural Computation 2016. Hammer B, Martinetz T, Villmann T (Eds); Machine Learning Reports, 11-18.
PUB | Download (ext.)
Linear Supervised Transfer Learning for Generalized Matrix LVQ.
In: Proceedings of the Workshop New Challenges in Neural Computation 2016. Hammer B, Martinetz T, Villmann T (Eds); Machine Learning Reports, 11-18.
2016 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2903457
Schleif F-M, Hammer B, Gonzalez Monroy J, Gonzalez Jimenez J, Blanco-Claraco J-L, Biehl M, Petkov N (2016)
Odor recognition in robotics applications by discriminative time-series modeling.
PATTERN ANALYSIS AND APPLICATIONS 19(1): 207-220.
PUB | DOI | WoS
Odor recognition in robotics applications by discriminative time-series modeling.
PATTERN ANALYSIS AND APPLICATIONS 19(1): 207-220.
2016 | Konferenzbeitrag | PUB-ID: 2909368
Geppert er, Hammer B (2016)
Incremental learning algorithms and applications.
In: ESANN. .
PUB
Incremental learning algorithms and applications.
In: ESANN. .
2016 | Konferenzbeitrag | PUB-ID: 2905195
Fischer L, Hammer B, Wersing H (2016)
Online Metric Learning for an Adaptation to Confidence Drift.
In: Proceedings of International Joint Conference on Neural Networks (IJCNN). Vancouver: IEEE: 748-755.
PUB
Online Metric Learning for an Adaptation to Confidence Drift.
In: Proceedings of International Joint Conference on Neural Networks (IJCNN). Vancouver: IEEE: 748-755.
2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2904178
Prahm C, Paaßen B, Schulz A, Hammer B, Aszmann O (2016)
Transfer Learning for Rapid Re-calibration of a Myoelectric Prosthesis after Electrode Shift.
In: Converging Clinical and Engineering Research on Neurorehabilitation II: Proceedings of the 3rd International Conference on NeuroRehabilitation (ICNR2016). Ibáñez J, Gonzáles-Vargas J, Azorín JM, Akay M, Pons JL (Eds); Springer: 153--157.
PUB
| PDF | DOI | Download (ext.)
Transfer Learning for Rapid Re-calibration of a Myoelectric Prosthesis after Electrode Shift.
In: Converging Clinical and Engineering Research on Neurorehabilitation II: Proceedings of the 3rd International Conference on NeuroRehabilitation (ICNR2016). Ibáñez J, Gonzáles-Vargas J, Azorín JM, Akay M, Pons JL (Eds); Springer: 153--157.
2016 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2910957
Biehl M, Hammer B, Villmann T (2016)
Prototype-based models in machine learning.
Wiley Interdisciplinary Reviews: Cognitive Science 7(2): 92-111.
PUB | DOI | WoS | PubMed | Europe PMC
Prototype-based models in machine learning.
Wiley Interdisciplinary Reviews: Cognitive Science 7(2): 92-111.
2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909366
Villmann T, Kaden M, Bohnsack A, Villmann JM, Drogies T, Saralajew S, Hammer B (2016)
Self-Adjusting Reject Options in Prototype Based Classification.
In: Advances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 11th International Workshop WSOM 2016, Houston, Texas, USA, January 6-8, 2016. Merényi E, Mendenhall MJ, O'Driscoll P (Eds); Advances in Intelligent Systems and Computing, 428. Cham: Springer International Publishing: 269-279.
PUB | DOI
Self-Adjusting Reject Options in Prototype Based Classification.
In: Advances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 11th International Workshop WSOM 2016, Houston, Texas, USA, January 6-8, 2016. Merényi E, Mendenhall MJ, O'Driscoll P (Eds); Advances in Intelligent Systems and Computing, 428. Cham: Springer International Publishing: 269-279.
2015 | Preprint | Veröffentlicht | PUB-ID: 2901613
Lux M, Hammer B, Sczyrba A (2015)
Automated Contamination Detection in Single-Cell Sequencing.
bioRxiv.
PUB
Automated Contamination Detection in Single-Cell Sequencing.
bioRxiv.
2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2783165
Hosseini B, Hammer B (2015)
Efficient Metric Learning for the Analysis of Motion Data.
In: 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA). Piscataway, NJ: IEEE.
PUB | DOI | Download (ext.) | arXiv
Efficient Metric Learning for the Analysis of Motion Data.
In: 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA). Piscataway, NJ: IEEE.
2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2724156
Paaßen B, Mokbel B, Hammer B (2015)
Adaptive structure metrics for automated feedback provision in Java programming.
In: Proceedings of the ESANN, 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); 307-312.
PUB
| PDF
Adaptive structure metrics for automated feedback provision in Java programming.
In: Proceedings of the ESANN, 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); 307-312.
2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2710031
Mokbel B, Paaßen B, Schleif F-M, Hammer B (2015)
Metric learning for sequences in relational LVQ.
Neurocomputing 169(SI): 306-322.
PUB
| PDF | DOI | Download (ext.) | WoS
Metric learning for sequences in relational LVQ.
Neurocomputing 169(SI): 306-322.
2015 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2900303
Schulz A, Hammer B (2015)
Visualization of Regression Models Using Discriminative Dimensionality Reduction.
In: Computer Analysis of Images and Patterns. Lecture Notes in Computer Science, 9257. Cham: Springer Science + Business Media: 437-449.
PUB
| PDF | DOI
Visualization of Regression Models Using Discriminative Dimensionality Reduction.
In: Computer Analysis of Images and Patterns. Lecture Notes in Computer Science, 9257. Cham: Springer Science + Business Media: 437-449.
2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900325
Blöbaum P, Schulz A, Hammer B (2015)
Unsupervised Dimensionality Reduction for Transfer Learning.
In: Proceedings. 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Louvain-la-Neuve: Ciaco: 507-512.
PUB
| PDF
Unsupervised Dimensionality Reduction for Transfer Learning.
In: Proceedings. 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Louvain-la-Neuve: Ciaco: 507-512.
2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900319
Schulz A, Hammer B (2015)
Discriminative dimensionality reduction for regression problems using the Fisher metric.
In: 2015 International Joint Conference on Neural Networks (IJCNN). Institute of Electrical & Electronics Engineers (IEEE): 1-8.
PUB | DOI
Discriminative dimensionality reduction for regression problems using the Fisher metric.
In: 2015 International Joint Conference on Neural Networks (IJCNN). Institute of Electrical & Electronics Engineers (IEEE): 1-8.
2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2774707
Fischer L, Hammer B, Wersing H (2015)
Certainty-based Prototype Insertion/Deletion for Classification with Metric Adaptation.
In: ESANN, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 7-12.
PUB
Certainty-based Prototype Insertion/Deletion for Classification with Metric Adaptation.
In: ESANN, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 7-12.
2015 | Konferenzbeitrag | PUB-ID: 2774721
Fischer L, Hammer B, Wersing H (2015)
Combining Offline and Online Classifiers for Life-long Learning.
In: IJCNN, International Joint Conference on Neural Networks. 2808-2815.
PUB
Combining Offline and Online Classifiers for Life-long Learning.
In: IJCNN, International Joint Conference on Neural Networks. 2808-2815.
2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2762087
Paaßen B, Mokbel B, Hammer B (2015)
A Toolbox for Adaptive Sequence Dissimilarity Measures for Intelligent Tutoring Systems.
In: Proceedings of the 8th International Conference on Educational Data Mining. Santos OC, Boticario JG, Romero C, Pechenizkiy M, Merceron A, Mitros P, Luna JM, Mihaescu C, Moreno P, Hershkovitz A, Ventura S, Desmarais M (Eds); International Educational Datamining Society: 632-632.
PUB | Download (ext.)
A Toolbox for Adaptive Sequence Dissimilarity Measures for Intelligent Tutoring Systems.
In: Proceedings of the 8th International Conference on Educational Data Mining. Santos OC, Boticario JG, Romero C, Pechenizkiy M, Merceron A, Mitros P, Luna JM, Mihaescu C, Moreno P, Hershkovitz A, Ventura S, Desmarais M (Eds); International Educational Datamining Society: 632-632.
2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2752948
Gross S, Mokbel B, Hammer B, Pinkwart N (2015)
Learning Feedback in Intelligent Tutoring Systems.
KI - Künstliche Intelligenz 29(4): 1-6.
PUB
| PDF | DOI | Download (ext.) | WoS
Learning Feedback in Intelligent Tutoring Systems.
KI - Künstliche Intelligenz 29(4): 1-6.
2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2752955
Walter O, Häb-Umbach R, Mokbel B, Paaßen B, Hammer B (2015)
Autonomous Learning of Representations.
KI - Künstliche Intelligenz 29(4): 339–351.
PUB
| PDF | DOI | Download (ext.) | WoS
Autonomous Learning of Representations.
KI - Künstliche Intelligenz 29(4): 339–351.
2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900320
Frenay B, Hofmann D, Schulz A, Biehl M, Hammer B (2014)
Valid interpretation of feature relevance for linear data mappings.
In: 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM). Institute of Electrical & Electronics Engineers (IEEE): 149-156.
PUB
| PDF | DOI
Valid interpretation of feature relevance for linear data mappings.
In: 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM). Institute of Electrical & Electronics Engineers (IEEE): 149-156.
2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2678214
Hofmann D, Schleif F-M, Paaßen B, Hammer B (2014)
Learning interpretable kernelized prototype-based models.
Neurocomputing 141: 84-96.
PUB | DOI | Download (ext.) | WoS
Learning interpretable kernelized prototype-based models.
Neurocomputing 141: 84-96.
2014 | Konferenzbeitrag | PUB-ID: 2909360
Gross S, Mokbel B, Hammer B, Pinkwart N (2014)
How to Select an Example? A Comparison of Selection Strategies in Example-Based Learning.
In: Intelligent Tutoring Systems. Trausan-Matu S, Elizabeth Boyer K, E. Crosby M, Panourgia K (Eds); Lecture Notes in Computer Science, 8474. Springer: 340-347.
PUB
How to Select an Example? A Comparison of Selection Strategies in Example-Based Learning.
In: Intelligent Tutoring Systems. Trausan-Matu S, Elizabeth Boyer K, E. Crosby M, Panourgia K (Eds); Lecture Notes in Computer Science, 8474. Springer: 340-347.
2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2774643
Fischer L, Nebel D, Villmann T, Hammer B, Wersing H (2014)
Rejection Strategies for Learning Vector Quantization – A Comparison of Probabilistic and Deterministic Approaches.
In: Advances in Self-Organizing Maps and Learning Vector Quantization. Villmann T, Schleif F-M, Kaden M, Lange M (Eds); Advances in Intelligent Systems and Computing, 295. Cham: Springer International Publishing: 109-118.
PUB | DOI
Rejection Strategies for Learning Vector Quantization – A Comparison of Probabilistic and Deterministic Approaches.
In: Advances in Self-Organizing Maps and Learning Vector Quantization. Villmann T, Schleif F-M, Kaden M, Lange M (Eds); Advances in Intelligent Systems and Computing, 295. Cham: Springer International Publishing: 109-118.
2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2673548
Fischer L, Hammer B, Wersing H (2014)
Rejection strategies for learning vector quantization.
In: ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Bruges, Belgium: i6doc.com: 41-46.
PUB
Rejection strategies for learning vector quantization.
In: ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Bruges, Belgium: i6doc.com: 41-46.
2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2774498
Fischer L, Hammer B, Wersing H (2014)
Local Rejection Strategies for Learning Vector Quantization.
In: Artificial Neural Networks and Machine Learning – ICANN 2014. Wermter S, Weber C, Duch W, Honkela T, Koprinkova-Hristova P, Magg S, Palm G, Villa AEP (Eds); Lecture Notes in Computer Science, 8681. Cham: Springer International Publishing: 563-570.
PUB | DOI
Local Rejection Strategies for Learning Vector Quantization.
In: Artificial Neural Networks and Machine Learning – ICANN 2014. Wermter S, Weber C, Duch W, Honkela T, Koprinkova-Hristova P, Magg S, Palm G, Villa AEP (Eds); Lecture Notes in Computer Science, 8681. Cham: Springer International Publishing: 563-570.
2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2673554
Mokbel B, Paaßen B, Hammer B (2014)
Adaptive distance measures for sequential data.
In: ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Bruges, Belgium: i6doc.com: 265-270.
PUB
| PDF
Adaptive distance measures for sequential data.
In: ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Bruges, Belgium: i6doc.com: 265-270.
2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2673559
Hammer B, He H, Martinetz T (2014)
Learning and modeling big data.
In: ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Bruges, Belgium: i6doc.com: 343-352.
PUB
Learning and modeling big data.
In: ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Bruges, Belgium: i6doc.com: 343-352.
2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2734058
Gross S, Mokbel B, Paaßen B, Hammer B, Pinkwart N (2014)
Example-based feedback provision using structured solution spaces.
International Journal of Learning Technology 9(3): 248-280.
PUB | DOI | Download (ext.)
Example-based feedback provision using structured solution spaces.
International Journal of Learning Technology 9(3): 248-280.
2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2710067
Mokbel B, Paaßen B, Hammer B (2014)
Efficient Adaptation of Structure Metrics in Prototype-Based Classification.
In: Artificial Neural Networks and Machine Learning - ICANN 2014 - 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings. Wermter S, Weber C, Duch W, Honkela T, Koprinkova-Hristova P, Magg S, Palm G, Villa A (Eds); Lecture Notes in Computer Science, 8681. Springer: 571-578.
PUB
| PDF | DOI | Download (ext.)
Efficient Adaptation of Structure Metrics in Prototype-Based Classification.
In: Artificial Neural Networks and Machine Learning - ICANN 2014 - 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings. Wermter S, Weber C, Duch W, Honkela T, Koprinkova-Hristova P, Magg S, Palm G, Villa A (Eds); Lecture Notes in Computer Science, 8681. Springer: 571-578.
2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2673545
Nebel D, Hammer B, Villmann T (2014)
Supervised Generative Models for Learning Dissimilarity Data.
In: ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Bruges, Belgium: i6doc.com: 35-40.
PUB
Supervised Generative Models for Learning Dissimilarity Data.
In: ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Bruges, Belgium: i6doc.com: 35-40.
2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2673557
Schulz A, Gisbrecht A, Hammer B (2014)
Relevance learning for dimensionality reduction.
In: ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Bruges, Belgium: i6doc.com: 165-170.
PUB
Relevance learning for dimensionality reduction.
In: ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Bruges, Belgium: i6doc.com: 165-170.
2014 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2900324
Gisbrecht A, Schulz A, Hammer B (2014)
Discriminative Dimensionality Reduction for the Visualization of Classifiers.
In: Pattern Recognition Applications and Methods. Advances in Intelligent Systems and Computing, 318. Cham: Springer Science + Business Media: 39-56.
PUB | DOI
Discriminative Dimensionality Reduction for the Visualization of Classifiers.
In: Pattern Recognition Applications and Methods. Advances in Intelligent Systems and Computing, 318. Cham: Springer Science + Business Media: 39-56.
2014 | Konferenzbeitrag | PUB-ID: 2909361
Hammer B, Nebel D, Riedel M, Villmann T (2014)
Generative versus Discriminative Prototype Based Classification.
In: Advances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 10th International Workshop, {WSOM} 2014, Mittweida, Germany, July, 2-4, 2014. Cham: Springer International Publishing: 123--132.
PUB | DOI
Generative versus Discriminative Prototype Based Classification.
In: Advances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 10th International Workshop, {WSOM} 2014, Mittweida, Germany, July, 2-4, 2014. Cham: Springer International Publishing: 123--132.
2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2623500
Gisbrecht A, Hammer B, Mokbel B, Sczyrba A (2013)
Nonlinear dimensionality reduction for cluster identification in metagenomic samples.
In: 17th International Conference on Information Visualisation IV 2013. Banissi E (Ed); Piscataway, NJ: IEEE: 174-179.
PUB | DOI
Nonlinear dimensionality reduction for cluster identification in metagenomic samples.
In: 17th International Conference on Information Visualisation IV 2013. Banissi E (Ed); Piscataway, NJ: IEEE: 174-179.
2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625185
Mokbel B, Gross S, Paaßen B, Pinkwart N, Hammer B (2013)
Domain-Independent Proximity Measures in Intelligent Tutoring Systems.
In: Proceedings of the 6th International Conference on Educational Data Mining (EDM). D'Mello SK, Calvo RA, Olney A (Eds); 334-335.
PUB | Download (ext.)
Domain-Independent Proximity Measures in Intelligent Tutoring Systems.
In: Proceedings of the 6th International Conference on Educational Data Mining (EDM). D'Mello SK, Calvo RA, Olney A (Eds); 334-335.
2013 | Konferenzbeitrag | PUB-ID: 2909358
Strickert M, Hammer B, Villmann T, Biehl M (2013)
Regularization and Improved Interpretation of Linear Data Mappings and Adaptive Distance Measures.
In: IEEE SSCI CIDM 2013. IEEE Computational Intelligence Society: 10-17.
PUB
Regularization and Improved Interpretation of Linear Data Mappings and Adaptive Distance Measures.
In: IEEE SSCI CIDM 2013. IEEE Computational Intelligence Society: 10-17.
2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622456
Schulz A, Gisbrecht A, Hammer B (2013)
Using Nonlinear Dimensionality Reduction to Visualize Classifiers.
In: Advances in computational intelligence. Proceedings. Vol 1. Rojas I, Joya G, Gabestany J (Eds); Lecture Notes in Computer Science, 7902. Springer: 59-68.
PUB | DOI | WoS
Using Nonlinear Dimensionality Reduction to Visualize Classifiers.
In: Advances in computational intelligence. Proceedings. Vol 1. Rojas I, Joya G, Gabestany J (Eds); Lecture Notes in Computer Science, 7902. Springer: 59-68.
2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622467
Schulz A, Gisbrecht A, Hammer B (2013)
Classifier inspection based on different discriminative dimensionality reductions.
In: Workshop NC^2 2013. TR Machine Learning Reports: 77-86.
PUB
Classifier inspection based on different discriminative dimensionality reductions.
In: Workshop NC^2 2013. TR Machine Learning Reports: 77-86.
2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625194
Gisbrecht A, Miche Y, Hammer B, Lendasse A (2013)
Visualizing Dependencies of Spectral Features using Mutual Information.
In: ESANN, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 573-578.
PUB
Visualizing Dependencies of Spectral Features using Mutual Information.
In: ESANN, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 573-578.
2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625199
Hofmann D, Hammer B (2013)
Sparse approximations for kernel learning vector quantization.
In: ESANN. .
PUB
Sparse approximations for kernel learning vector quantization.
In: ESANN. .
2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625202
Schleif F-M, Zhu X, Hammer B (2013)
Sparse prototype representation by core sets.
In: IDEAL 2013. Hujun Yin et.al (Ed);.
PUB
Sparse prototype representation by core sets.
In: IDEAL 2013. Hujun Yin et.al (Ed);.
2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625207
Gross S, Mokbel B, Hammer B, Pinkwart N (2013)
Towards Providing Feedback to Students in Absence of Formalized Domain Models.
In: AIED. 644-648.
PUB
Towards Providing Feedback to Students in Absence of Formalized Domain Models.
In: AIED. 644-648.
2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615701
Zhu X, Schleif F-M, Hammer B (2013)
Semi-Supervised Vector Quantization for proximity data.
In: Proceedings of ESANN 2013. 89-94.
PUB
Semi-Supervised Vector Quantization for proximity data.
In: Proceedings of ESANN 2013. 89-94.
2013 | Konferenzbeitrag | PUB-ID: 2909359
Nebel D, Hammer B, Villmann T (2013)
A Median Variant of Generalized Learning Vector Quantization.
In: ICONIP (2). 19-26.
PUB
A Median Variant of Generalized Learning Vector Quantization.
In: ICONIP (2). 19-26.
2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2625232
Gisbrecht A, Mokbel B, Schleif F-M, Zhu X, Hammer B (2012)
Linear Time Relational Prototype Based Learning.
Int. J. Neural Syst. 22(5): 1250021.
PUB | DOI | WoS | PubMed | Europe PMC
Linear Time Relational Prototype Based Learning.
Int. J. Neural Syst. 22(5): 1250021.
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622449
Schulz A, Gisbrecht A, Bunte K, Hammer B (2012)
How to visualize a classifier?
In: Proceedings of the Workshop - New Challenges in Neural Computation 2012. Machine Learning Reports: 73-83.
PUB
How to visualize a classifier?
In: Proceedings of the Workshop - New Challenges in Neural Computation 2012. Machine Learning Reports: 73-83.
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625260
Gisbrecht A, Lueks W, Mokbel B, Hammer B (2012)
Out-of-sample kernel extensions for nonparametric dimensionality reduction.
In: ESANN 2012. 531-536.
PUB
Out-of-sample kernel extensions for nonparametric dimensionality reduction.
In: ESANN 2012. 531-536.
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625265
Gisbrecht A, Sovilj D, Hammer B, Lendasse A (2012)
Relevance learning for time series inspection.
In: ESANN 2012. Verleysen M (Ed); 489-494.
PUB
Relevance learning for time series inspection.
In: ESANN 2012. Verleysen M (Ed); 489-494.
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2671172
Hofmann D, Gisbrecht A, Hammer B (2012)
Discriminative probabilistic prototype based models in kernel space.
In: Workshop NC^2 2012. TR Machine Learning Reports.
PUB
Discriminative probabilistic prototype based models in kernel space.
In: Workshop NC^2 2012. TR Machine Learning Reports.
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2536426
Mokbel B, Gross S, Lux M, Pinkwart N, Hammer B (2012)
How to Quantitatively Compare Data Dissimilarities for Unsupervised Machine Learning?
In: Artificial Neural Networks in Pattern Recognition. 5th INNS IAPR TC 3 GIRPR Workshop, ANNPR 2012. Proceedings. Mana N, Schwenker F, Trentin E (Eds); Lecture Notes in Artificial Intelligence, 7477. Springer Berlin Heidelberg: 1-13.
PUB
| PDF | DOI
How to Quantitatively Compare Data Dissimilarities for Unsupervised Machine Learning?
In: Artificial Neural Networks in Pattern Recognition. 5th INNS IAPR TC 3 GIRPR Workshop, ANNPR 2012. Proceedings. Mana N, Schwenker F, Trentin E (Eds); Lecture Notes in Artificial Intelligence, 7477. Springer Berlin Heidelberg: 1-13.
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625238
Hofmann D, Gisbrecht A, Hammer B (2012)
Efficient Approximations of Kernel Robust Soft LVQ.
In: WSOM. .
PUB
Efficient Approximations of Kernel Robust Soft LVQ.
In: WSOM. .
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625271
Bouveyron C, Hammer B, Villmann T (2012)
Recent developments in clustering algorithms.
In: ESANN 2012. Verleysen M (Ed); 447-458.
PUB
Recent developments in clustering algorithms.
In: ESANN 2012. Verleysen M (Ed); 447-458.
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625276
Gisbrecht A, Mokbel B, Hammer B (2012)
Linear Basis-Function t-SNE for Fast Nonlinear Dimensionality Reduction.
In: IJCNN. .
PUB
Linear Basis-Function t-SNE for Fast Nonlinear Dimensionality Reduction.
In: IJCNN. .
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625242
Gross S, Mokbel B, Hammer B, Pinkwart N (2012)
Feedback Provision Strategies in Intelligent Tutoring Systems Based on Clustered Solution Spaces.
In: DeLFI. 27-38.
PUB
Feedback Provision Strategies in Intelligent Tutoring Systems Based on Clustered Solution Spaces.
In: DeLFI. 27-38.
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625247
Gisbrecht A, Hofmann D, Hammer B (2012)
Discriminative Dimensionality Reduction Mappings.
In: Advances in Intelligent Data Analysis XI - 11th International Symposium, IDA 2012, Helsinki, Finland, October 25-27, 2012. Proceedings. Hollmén J, Klawonn F, Tucker A (Eds); Lecture Notes in Computer Science, 7619. Springer: 126-138.
PUB
Discriminative Dimensionality Reduction Mappings.
In: Advances in Intelligent Data Analysis XI - 11th International Symposium, IDA 2012, Helsinki, Finland, October 25-27, 2012. Proceedings. Hollmén J, Klawonn F, Tucker A (Eds); Lecture Notes in Computer Science, 7619. Springer: 126-138.
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625254
Hofmann D, Hammer B (2012)
Kernel Robust Soft Learning Vector Quantization.
In: Artificial Neural Networks in Pattern Recognition - 5th INNS IAPR TC 3 GIRPR Workshop, ANNPR 2012, Trento, Italy, September 17-19, 2012. Proceedings. Mana N, Schwenker F, Trentin E (Eds); Lecture Notes in Computer Science, 7477. Springer: 14-23.
PUB
Kernel Robust Soft Learning Vector Quantization.
In: Artificial Neural Networks in Pattern Recognition - 5th INNS IAPR TC 3 GIRPR Workshop, ANNPR 2012, Trento, Italy, September 17-19, 2012. Proceedings. Mana N, Schwenker F, Trentin E (Eds); Lecture Notes in Computer Science, 7477. Springer: 14-23.
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615750
Schleif F-M, Zhu X, Gisbrecht A, Hammer B (2012)
Fast approximated relational and kernel clustering.
In: Proceedings of ICPR 2012. IEEE: 1229-1232.
PUB
Fast approximated relational and kernel clustering.
In: Proceedings of ICPR 2012. IEEE: 1229-1232.
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2536437
Gross S, Zhu X, Hammer B, Pinkwart N (2012)
Cluster based feedback provision strategies in intelligent tutoring systems.
In: Proceedings of the 11th international conference on Intelligent Tutoring Systems. Berlin, Heidelberg: Springer-Verlag: 699-700.
PUB
| PDF | DOI | Download (ext.)
Cluster based feedback provision strategies in intelligent tutoring systems.
In: Proceedings of the 11th international conference on Intelligent Tutoring Systems. Berlin, Heidelberg: Springer-Verlag: 699-700.
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2536444
Gross S, Mokbel B, Hammer B, Pinkwart N (2012)
Feedback Provision Strategies in Intelligent Tutoring Systems Based on Clustered Solution Spaces.
In: DeLFI 2012: Die 10. e-Learning Fachtagung Informatik. Desel J, Haake JM, Spannagel C, Gesellschaft für Informatik (Eds); GI-Edition : Proceedings, 207. Hagen, Germany: Köllen: 27-38.
PUB
| PDF
Feedback Provision Strategies in Intelligent Tutoring Systems Based on Clustered Solution Spaces.
In: DeLFI 2012: Die 10. e-Learning Fachtagung Informatik. Desel J, Haake JM, Spannagel C, Gesellschaft für Informatik (Eds); GI-Edition : Proceedings, 207. Hagen, Germany: Köllen: 27-38.
2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2534839
Gisbrecht A, Mokbel B, Schleif F-M, Zhu X, Hammer B (2012)
Linear Time Relational Prototype Based Learning.
Int. J. Neural Syst. 22(05): 1250021.
PUB | DOI | WoS | PubMed | Europe PMC
Linear Time Relational Prototype Based Learning.
Int. J. Neural Syst. 22(05): 1250021.
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534877
Schleif F-M, Mokbel B, Gisbrecht A, Theunissen L, Dürr V, Hammer B (2012)
Learning Relevant Time Points for Time-Series Data in the Life Sciences.
In: ICANN (2). Lecture Notes in Computer Science, 7553. Berlin, Heidelberg: Springer Berlin Heidelberg: 531-539.
PUB | DOI
Learning Relevant Time Points for Time-Series Data in the Life Sciences.
In: ICANN (2). Lecture Notes in Computer Science, 7553. Berlin, Heidelberg: Springer Berlin Heidelberg: 531-539.
2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2489405
Bunte K, Schneider P, Hammer B, Schleif F-M, Villmann T, Biehl M (2012)
Limited Rank Matrix Learning, discriminative dimension reduction and visualization.
Neural Networks 26: 159-173.
PUB | DOI | WoS | PubMed | Europe PMC
Limited Rank Matrix Learning, discriminative dimension reduction and visualization.
Neural Networks 26: 159-173.
2012 | Konferenzbeitrag | PUB-ID: 2909356
Mokbel B, Lueks W, Gisbrecht A, Biehl M, Hammer B (2012)
Visualizing the quality of dimensionality reduction.
In: ESANN 2012. Verleysen M (Ed); 179--184.
PUB
Visualizing the quality of dimensionality reduction.
In: ESANN 2012. Verleysen M (Ed); 179--184.
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534905
Schleif F-M, Gisbrecht A, Hammer B (2012)
Relevance learning for short high-dimensional time series in the life sciences.
In: IJCNN. IEEE Computational Intelligence Society, Institute of Electrical and Electronics Engineers (Eds); Piscataway, NJ: IEEE: 1-8.
PUB | DOI
Relevance learning for short high-dimensional time series in the life sciences.
In: IJCNN. IEEE Computational Intelligence Society, Institute of Electrical and Electronics Engineers (Eds); Piscataway, NJ: IEEE: 1-8.
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276480
Gisbrecht A, Schleif F-M, Zhu X, Hammer B (2011)
Linear time heuristics for topographic mapping of dissimilarity data.
In: Intelligent Data Engineering and Automated Learning - IDEAL 2011: IDEAL 2011, 12th international conference, Norwich, UK, September 7 - 9, 2011 ; proceedings. Lecture Notes in Computer Science, 6936. Berlin, Heidelberg: Springer: 25-33.
PUB | DOI
Linear time heuristics for topographic mapping of dissimilarity data.
In: Intelligent Data Engineering and Automated Learning - IDEAL 2011: IDEAL 2011, 12th international conference, Norwich, UK, September 7 - 9, 2011 ; proceedings. Lecture Notes in Computer Science, 6936. Berlin, Heidelberg: Springer: 25-33.
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276485
Hammer B, Gisbrecht A, Hasenfuss A, Mokbel B, Schleif F-M, Zhu X (2011)
Topographic Mapping of Dissimilarity Data.
In: WSOM'11. .
PUB
Topographic Mapping of Dissimilarity Data.
In: WSOM'11. .
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276492
Schleif F-M, Gisbrecht A, Hammer B (2011)
Accelerating Kernel Neural Gas.
In: ICANN'2011. Kaski S, Honkela T, Gitolami M, Dutch W (Eds);.
PUB
Accelerating Kernel Neural Gas.
In: ICANN'2011. Kaski S, Honkela T, Gitolami M, Dutch W (Eds);.
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276500
Kaestner M, Hammer B, Biehl M, Villmann T (2011)
Generalized Functional Relevance Learning Vector Quantization.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); D side: pp. 93-98.
PUB
Generalized Functional Relevance Learning Vector Quantization.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); D side: pp. 93-98.
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276512
Hammer B, Biehl M, Bunte K, Mokbel B (2011)
A general framework for dimensionality reduction for large data sets.
In: WSOM'11. .
PUB
A general framework for dimensionality reduction for large data sets.
In: WSOM'11. .
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276517
Bunte K, Biehl M, Hammer B (2011)
Supervised dimension reduction mappings.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); D side: pp. 281-286.
PUB
Supervised dimension reduction mappings.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); D side: pp. 281-286.
2011 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2309980
Schleif F-M, Villmann T, Hammer B, Schneider P (2011)
Efficient Kernelized Prototype-based Classification.
International Journal of Neural Systems 21(06): 443-457.
PUB | DOI | WoS | PubMed | Europe PMC
Efficient Kernelized Prototype-based Classification.
International Journal of Neural Systems 21(06): 443-457.
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276522
Gisbrecht A, Hammer B, Schleif F-M, Zhu X (2011)
Accelerating dissimilarity clustering for biomedical data analysis.
In: IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. pp.154-161.
PUB
Accelerating dissimilarity clustering for biomedical data analysis.
In: IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. pp.154-161.
2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2091665
Zhu X, Hammer B (2011)
Patch Affinity Propagation.
Presented at the 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium.
PUB
Patch Affinity Propagation.
Presented at the 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium.
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276543
Gisbrecht A, Mokbel B, Hammer B (2010)
The Nystrom approximation for relational generative topographic mappings.
In: NIPS workshop on challenges of Data Visualization. .
PUB
The Nystrom approximation for relational generative topographic mappings.
In: NIPS workshop on challenges of Data Visualization. .
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994127
Villmann T, Haase S, Schleif F-M, Hammer B (2010)
Divergence Based Online Learning in Vector Quantization.
In: Artificial Intelligence and Soft Computing. Lecture Notes in Computer Science, 6113. Rutkowski L, Scherer R, Tadeusiewicz R, Zadeh L, Zurada J (Eds); Berlin, Heidelberg: Springer: 479-486.
PUB | DOI
Divergence Based Online Learning in Vector Quantization.
In: Artificial Intelligence and Soft Computing. Lecture Notes in Computer Science, 6113. Rutkowski L, Scherer R, Tadeusiewicz R, Zadeh L, Zurada J (Eds); Berlin, Heidelberg: Springer: 479-486.
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1796018
Arnonkijpanich B, Hasenfuss A, Hammer B (2010)
Local matrix learning in clustering and applications for manifold visualization.
Neural Networks 23(4): 476-486.
PUB | DOI | WoS | PubMed | Europe PMC
Local matrix learning in clustering and applications for manifold visualization.
Neural Networks 23(4): 476-486.
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993273
Arnonkijpanich B, Hammer B (2010)
Global Coordination based on Matrix Neural Gas for Dynamic Texture Synthesis.
In: ANNPR'2010. Lecture Notes in Artificial Intelligence, 5998. El Gayar N, Schwenker F (Eds); Springer: 84-95.
PUB
Global Coordination based on Matrix Neural Gas for Dynamic Texture Synthesis.
In: ANNPR'2010. Lecture Notes in Artificial Intelligence, 5998. El Gayar N, Schwenker F (Eds); Springer: 84-95.
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993367
Bunte K, Hammer B, Villmann T, Biehl M, Wismüller A (2010)
Exploratory Observation Machine (XOM) with Kullback-Leibler Divergence for Dimensionality Reduction and Visualization.
In: ESANN'10. Proceedings of the 18th European Symposium on Artificial Neural Networks. Verleysen M (Ed); Evere: D side: 87-92.
PUB
Exploratory Observation Machine (XOM) with Kullback-Leibler Divergence for Dimensionality Reduction and Visualization.
In: ESANN'10. Proceedings of the 18th European Symposium on Artificial Neural Networks. Verleysen M (Ed); Evere: D side: 87-92.
2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1929672
Witoelar AW, Ghosh A, de Vries JJG, Hammer B, Biehl M (2010)
Window-Based Example Selection in Learning Vector Quantization.
Neural Computing 22(11): 2924-2961.
PUB | DOI | WoS | PubMed | Europe PMC
Window-Based Example Selection in Learning Vector Quantization.
Neural Computing 22(11): 2924-2961.
2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1794373
Hammer B, Hasenfuss A (2010)
Topographic Mapping of Large Dissimilarity Data Sets.
Neural Computation 22(9): 2229-2284.
PUB | DOI | WoS | PubMed | Europe PMC
Topographic Mapping of Large Dissimilarity Data Sets.
Neural Computation 22(9): 2229-2284.
2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1795962
Schneider P, Bunte K, Stiekema H, Hammer B, Villmann T, Biehl M (2010)
Regularization in Matrix Relevance Learning.
IEEE Transactions on Neural Networks 21(5): 831-840.
PUB | DOI | WoS | PubMed | Europe PMC
Regularization in Matrix Relevance Learning.
IEEE Transactions on Neural Networks 21(5): 831-840.
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993978
Schleif F-M, Villmann T, Hammer B, Schneider P, Biehl M (2010)
Generalized derivative based Kernelized learning vector quantization.
In: Intelligent Data Engineering and Automated Learning – IDEAL 2010 11th International Conference, Paisley, UK, September 1-3, 2010. Proceedings. Fyfe C, Tino P, Charles D, Garcia-Osorio C, Yin H (Eds); Berlin u.a.: Springer: 21-28.
PUB | DOI
Generalized derivative based Kernelized learning vector quantization.
In: Intelligent Data Engineering and Automated Learning – IDEAL 2010 11th International Conference, Paisley, UK, September 1-3, 2010. Proceedings. Fyfe C, Tino P, Charles D, Garcia-Osorio C, Yin H (Eds); Berlin u.a.: Springer: 21-28.
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993536
Hammer B, Hasenfuss A (2010)
Clustering very large dissimilarity data sets.
In: Artificial Neural Networks in Pattern Recognition (ANNPR 2010). Proceedings. Schwenker F, El Gayar N (Eds); Lecture Notes in Artificial Intelligence, 5998. Berlin: Springer: 259-273.
PUB | DOI
Clustering very large dissimilarity data sets.
In: Artificial Neural Networks in Pattern Recognition (ANNPR 2010). Proceedings. Schwenker F, El Gayar N (Eds); Lecture Notes in Artificial Intelligence, 5998. Berlin: Springer: 259-273.
2010 | Konferenzband | Veröffentlicht | PUB-ID: 2276535
Hammer B, Hitzler P, Maass W, Toussaint M (Eds) (2010)
Learning paradigms in dynamic environments, 25.07.10-30.07.20.; 10302.
Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany.
PUB
Learning paradigms in dynamic environments, 25.07.10-30.07.20.; 10302.
Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany.
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276547
Mokbel B, Gisbrecht A, Hammer B (2010)
On the effect of clustering on quality assessment measures for dimensionality reduction.
In: NIPS workshop on Challenges of Data Visualization. .
PUB
On the effect of clustering on quality assessment measures for dimensionality reduction.
In: NIPS workshop on Challenges of Data Visualization. .
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993448
Gisbrecht A, Hammer B (2010)
Relevance learning in generative topographic maps.
In: ESANN'10. Verleysen M (Ed); Evere: D side: 387-392.
PUB
Relevance learning in generative topographic maps.
In: ESANN'10. Verleysen M (Ed); Evere: D side: 387-392.
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993452
Gisbrecht A, Mokbel B, Hammer B (2010)
Relational Generative Topographic Map.
In: ESANN'10. Verleysen M (Ed); Evere: D side: 277-282.
PUB
Relational Generative Topographic Map.
In: ESANN'10. Verleysen M (Ed); Evere: D side: 277-282.
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993457
Gisbrecht A, Mokbel B, Hasenfuss A, Hammer B (2010)
Visualizing Dissimilarity Data using generative topographic mapping.
In: KI'2010. Dillmann R, Beyerer J, Hanebeck UD, Schulz T (Eds); 227-237.
PUB
Visualizing Dissimilarity Data using generative topographic mapping.
In: KI'2010. Dillmann R, Beyerer J, Hanebeck UD, Schulz T (Eds); 227-237.
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994138
Villmann T, Haase S, Schleif F-M, Hammer B, Biehl M (2010)
The Mathematics of Divergence Based Online Learning in Vector Quanitzation.
In: ANNPR'2010. El Gayar N, Schwenker F (Eds); Berlin, Heidelberg: Springer: 108-119.
PUB
The Mathematics of Divergence Based Online Learning in Vector Quanitzation.
In: ANNPR'2010. El Gayar N, Schwenker F (Eds); Berlin, Heidelberg: Springer: 108-119.
2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994227
Villmann T, Schleif F-M, Hammer B (2010)
Sparse representation of data.
In: ESANN'10. Verleysen M (Ed); D side: 225-234.
PUB
Sparse representation of data.
In: ESANN'10. Verleysen M (Ed); D side: 225-234.
2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993679
Hammer B, Schrauwen B, Steil JJ (2009)
Recent advances in efficient learning of recurrent networks.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); Brugge: d-facto: 213-226.
PUB
Recent advances in efficient learning of recurrent networks.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); Brugge: d-facto: 213-226.
2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993984
Schleif F-M, Villmann T, Kostrzewa M, Hammer B, Gammerman A (2009)
Cancer Informatics by Prototype-networks in Mass Spectrometry.
Artificial Intelligence in Medicine 45(2-3): 215-228.
PUB | DOI | WoS | PubMed | Europe PMC
Cancer Informatics by Prototype-networks in Mass Spectrometry.
Artificial Intelligence in Medicine 45(2-3): 215-228.
2009 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1994160
Villmann T, Hammer B, Biehl M (2009)
Some theoretical aspects of the neural gas vector quantizer.
In: Similarity Based Clustering. Biehl M, Hammer B, Verleysen M, Villmann T (Eds); Lecture Notes Artificial Intelligence, 5400, Berlin, Heidelberg: Springer: 23-34.
PUB | DOI
Some theoretical aspects of the neural gas vector quantizer.
In: Similarity Based Clustering. Biehl M, Hammer B, Verleysen M, Villmann T (Eds); Lecture Notes Artificial Intelligence, 5400, Berlin, Heidelberg: Springer: 23-34.
2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994305
Witolaer A, Biehl M, Hammer B (2009)
Equilibrium properties of offline LVQ.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); d-side publications: 535-540.
PUB
Equilibrium properties of offline LVQ.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); d-side publications: 535-540.
2009 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1993326
Biehl M, Hammer B, Schneider P, Villmann T (2009)
Metric learning for prototype based classification.
In: Innovations in Neural Information – Paradigms and Applications. Bianchini M, Maggini M, Scarselli F (Eds); Studies in Computational Intelligence, 247, Berlin: Springer: 183-199.
PUB | DOI
Metric learning for prototype based classification.
In: Innovations in Neural Information – Paradigms and Applications. Bianchini M, Maggini M, Scarselli F (Eds); Studies in Computational Intelligence, 247, Berlin: Springer: 183-199.
2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993994
Schneider P, Biehl M, Hammer B (2009)
Hyperparameter Learning in robust soft LVQ.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); d-side publications: 517-522.
PUB
Hyperparameter Learning in robust soft LVQ.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); d-side publications: 517-522.
2009 | Konferenzband | Veröffentlicht | PUB-ID: 1994310
Biehl M, Hammer B, Hochreiter S, Kremer SC, Villmann T (Eds) (2009)
Similarity-based learning on structures.; 9081.
Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany.
PUB
Similarity-based learning on structures.; 9081.
Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany.
2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994008
Schneider P, Biehl M, Hammer B (2009)
Distance learning in discriminative vector quantization.
Neural Computation 21(10): 2942-2969.
PUB | DOI | WoS | PubMed | Europe PMC
Distance learning in discriminative vector quantization.
Neural Computation 21(10): 2942-2969.
2009 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1993555
Hammer B, Hasenfuss A, Rossi F (2009)
Median topographic maps for biological data sets.
In: Similarity Based Clustering. Biehl M, Hammer B, Verleysen M, Villmann T (Eds); Lecture Notes Artificial Intelligence, 5400, Berlin, Heidelberg: Springer: 92-117.
PUB | DOI
Median topographic maps for biological data sets.
In: Similarity Based Clustering. Biehl M, Hammer B, Verleysen M, Villmann T (Eds); Lecture Notes Artificial Intelligence, 5400, Berlin, Heidelberg: Springer: 92-117.
2009 | Report | Veröffentlicht | PUB-ID: 1993316
Biehl M, Hammer B, Schleif F-M, Schneider P, Villmann T (2009)
Stationarity of Matrix Relevance Learning Vector Quantization. Machine Learning Reports.
Leipzig: Universität Leipzig.
PUB
Stationarity of Matrix Relevance Learning Vector Quantization. Machine Learning Reports.
Leipzig: Universität Leipzig.
2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993361
Bunte K, Hammer B, Biehl M (2009)
Nonlinear dimension reduction and visualization of labeled data.
In: International Conference on Computer Analysis of Images and Patterns. Jiang X, Petkov N (Eds); Lecture Notes in Computer Science, 5702, 5702. Berlin: Springer: 1162-1170.
PUB | DOI
Nonlinear dimension reduction and visualization of labeled data.
In: International Conference on Computer Analysis of Images and Patterns. Jiang X, Petkov N (Eds); Lecture Notes in Computer Science, 5702, 5702. Berlin: Springer: 1162-1170.
2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993429
Geweniger T, Zühlke D, Hammer B, Villmann T (2009)
Median variant of fuzzy-c-means.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); Evere: d-side publications: 523-528.
PUB
Median variant of fuzzy-c-means.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); Evere: d-side publications: 523-528.
2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993835
Mokbel B, Hasenfuss A, Hammer B (2009)
Graph-based Representation of Symbolic Musical Data.
In: Graph-Based Representation in Pattern Recognition (GbRPR 2009). Lecture Notes in Computer Science, 5534. Torsello A, Escolano F, Brun L, International Association for Pattern Recognition. Technical Committee on Graph Based Representations (Eds); Lecture notes in computer science, 5534. Berlin: Springer: 42-51.
PUB | DOI
Graph-based Representation of Symbolic Musical Data.
In: Graph-Based Representation in Pattern Recognition (GbRPR 2009). Lecture Notes in Computer Science, 5534. Torsello A, Escolano F, Brun L, International Association for Pattern Recognition. Technical Committee on Graph Based Representations (Eds); Lecture notes in computer science, 5534. Berlin: Springer: 42-51.
2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994004
Schneider P, Biehl M, Hammer B (2009)
Adaptive relevance matrices in learning vector quantization.
Neural Computation 21(12): 3532-3561.
PUB | DOI | WoS | PubMed | Europe PMC
Adaptive relevance matrices in learning vector quantization.
Neural Computation 21(12): 3532-3561.
2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993356
Bunte K, Biehl M, Hammer B (2009)
Nonlinear discriminative data visualization.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); Evere: d-side publications: 65-70.
PUB
Nonlinear discriminative data visualization.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); Evere: d-side publications: 65-70.
2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994152
Villmann T, Hammer B (2009)
Functional principal component learning using Oja's method and Sobolev norms.
In: Advances in Self-Organizing Maps. Principe JC, Miikkulainen R (Eds); 325-333.
PUB
Functional principal component learning using Oja's method and Sobolev norms.
In: Advances in Self-Organizing Maps. Principe JC, Miikkulainen R (Eds); 325-333.
2008 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1993939
Schleif F-M, Villmann T, Hammer B (2008)
Pattern Recognition by Supervised Relevance Neural Gas and its Application to Spectral Data in Bioinformatics.
In: Encyclopedia of Artificial Intelligence. Dopico JR-n R-al, Dorado J, Pazos A (Eds); IGI Global: 1337-1342.
PUB
Pattern Recognition by Supervised Relevance Neural Gas and its Application to Spectral Data in Bioinformatics.
In: Encyclopedia of Artificial Intelligence. Dopico JR-n R-al, Dorado J, Pazos A (Eds); IGI Global: 1337-1342.
2008 | Konferenzband | Veröffentlicht | PUB-ID: 1994329
de Raedt L, Hammer B, Hitzler P, Maass W (Eds) (2008)
Recurrent Neural Networks - Models, Capacities, and Applications.; 8041.
Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI).
PUB
Recurrent Neural Networks - Models, Capacities, and Applications.; 8041.
Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI).
2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993282
Arnonkijpanich B, Hammer B, Hasenfuss A, Lursinsap C (2008)
Matrix Learning for Topographic Neural Maps.
In: ICANN (1). Lecture Notes in Computer Science, 5163. Kurková V, Neruda R, Koutn'ık J (Eds); Berlin: Springer: 572-582.
PUB
Matrix Learning for Topographic Neural Maps.
In: ICANN (1). Lecture Notes in Computer Science, 5163. Kurková V, Neruda R, Koutn'ık J (Eds); Berlin: Springer: 572-582.
2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993261
Alex N, Hammer B (2008)
Parallelizing single pass patch clustering.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); Evere, Belgium: d-side publications: 227-232.
PUB
Parallelizing single pass patch clustering.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); Evere, Belgium: d-side publications: 227-232.
2008 | Report | Veröffentlicht | PUB-ID: 1993278
Arnonkijpanich B, Hammer B, Hasenfuss A (2008)
Local Matrix Adaptation in Topographic Neural Maps. IfI-08-07.
Clausthal-Zellerfeld: Clausthal University of Technology.
PUB
Local Matrix Adaptation in Topographic Neural Maps. IfI-08-07.
Clausthal-Zellerfeld: Clausthal University of Technology.
2008 | Report | Veröffentlicht | PUB-ID: 1993379
Bunte K, Schneider P, Hammer B, Schleif F-M, Villmann T, Biehl M (2008)
Discriminative Visualization by Limited Rank Matrix Learning. Machine Learning Reports.
Leipzig: Universität Leipzig.
PUB
Discriminative Visualization by Limited Rank Matrix Learning. Machine Learning Reports.
Leipzig: Universität Leipzig.
2008 | Report | Veröffentlicht | PUB-ID: 1994012
Schneider P, Biehl M, Hammer B (2008)
Matrix Adaptation in Discriminative Vector Quantization. IfI Technical Report Seriess.
Clausthal-Zellerfeld: Clausthal University of Technology.
PUB
Matrix Adaptation in Discriminative Vector Quantization. IfI Technical Report Seriess.
Clausthal-Zellerfeld: Clausthal University of Technology.
2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993788
Hasenfuss A, Hammer B (2008)
Single Pass Clustering and Classification of Large Dissimilarity Datasets.
In: Artificial Intelligence and Pattern Recognition. Prasad B, Sinha P, Ram A, Kerre EE (Eds); ISRST: 219-223.
PUB
Single Pass Clustering and Classification of Large Dissimilarity Datasets.
In: Artificial Intelligence and Pattern Recognition. Prasad B, Sinha P, Ram A, Kerre EE (Eds); ISRST: 219-223.
2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994072
Strickert M, Schneider P, Keilwagen J, Villmann T, Biehl M, Hammer B (2008)
Discriminatory Data Mapping by Matrix-Based Supervised Learning Metrics.
In: Artificial Neural Networks in Pattern Recognition. Third IAPR Workshop. Proceedings. Prevost L, Marinai S, Schwenker F (Eds); Lecture Notes in Computer Science, 5064, Berlin: Springer: 78-89.
PUB | DOI
Discriminatory Data Mapping by Matrix-Based Supervised Learning Metrics.
In: Artificial Neural Networks in Pattern Recognition. Third IAPR Workshop. Proceedings. Prevost L, Marinai S, Schwenker F (Eds); Lecture Notes in Computer Science, 5064, Berlin: Springer: 78-89.
2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994089
Strickert M, Sreenivasulu N, Villmann T, Hammer B (2008)
Robust Centroid-Based Clustering using Derivatives of Pearson Correlation.
In: BIOSIGNALS (2). Encarnação P, Veloso A (Eds); INSTICC - Institute for Systems and Technologies of Information, Control and Communication: 197-203.
PUB
Robust Centroid-Based Clustering using Derivatives of Pearson Correlation.
In: BIOSIGNALS (2). Encarnação P, Veloso A (Eds); INSTICC - Institute for Systems and Technologies of Information, Control and Communication: 197-203.
2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994281
Winkler T, Drieseberg J, Hasenfuß A, Hammer B, Hormann K (2008)
Thinning Mesh Animations.
In: Proceedings of Vision, Modeling, and Visualization 2008. Deussen O, Keim D, Saupe D (Eds); Konstanz, Germany: Aka: 149-158.
PUB
Thinning Mesh Animations.
In: Proceedings of Vision, Modeling, and Visualization 2008. Deussen O, Keim D, Saupe D (Eds); Konstanz, Germany: Aka: 149-158.
2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993804
Hasenfuss A, Hammer B, Rossi F (2008)
Patch Relational Neural Gas - Clustering of Huge Dissimilarity Datasets.
In: Artificial Neural Networks in Pattern Recognition, Third IAPR Workshop. Proceedings. Lecture Notes in Computer Science, 5064. Prevost L, Marinai S, Schwenker F (Eds); Berlin: Springer: 1-12.
PUB | DOI
Patch Relational Neural Gas - Clustering of Huge Dissimilarity Datasets.
In: Artificial Neural Networks in Pattern Recognition, Third IAPR Workshop. Proceedings. Lecture Notes in Computer Science, 5064. Prevost L, Marinai S, Schwenker F (Eds); Berlin: Springer: 1-12.
2008 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1993900
Schleif F-M, Hammer B, Villmann T (2008)
Analysis of Spectral Data in Clinical Proteomics by use of Learning Vector Quantizers.
In: Computational Intelligence in Biomedicine and Bioinformatics: Current Trends and Applications. Van de Werff M, Delder A, Tollenaar R (Eds); Berlin: Springer: 141-167.
PUB | DOI
Analysis of Spectral Data in Clinical Proteomics by use of Learning Vector Quantizers.
In: Computational Intelligence in Biomedicine and Bioinformatics: Current Trends and Applications. Van de Werff M, Delder A, Tollenaar R (Eds); Berlin: Springer: 141-167.
2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993798
Hasenfuss A, Hammer B, Geweniger T, Villmann T (2008)
Magnification Control in Relational Neural Gas.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); Brussels: d-side publications: 325-330.
PUB
Magnification Control in Relational Neural Gas.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); Brussels: d-side publications: 325-330.
2008 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994253
Villmann T, Schleif F-M, Kostrzewa M, Walch A, Hammer B (2008)
Classification of mass-spectrometric data in clinical proteomics using learning vector quantization methods.
Briefings in Bioinformatics 9(2): 129-143.
PUB | DOI | WoS | PubMed | Europe PMC
Classification of mass-spectrometric data in clinical proteomics using learning vector quantization methods.
Briefings in Bioinformatics 9(2): 129-143.
2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2001836
Geweniger T, Schleif F-M, Hasenfuss A, Hammer B, Villmann T (2008)
Comparison of cluster algorithms for the analysis of text data using Kolmogorov complexity.
In: ICONIP 2008. Köppen M, Kasabov NK, Coghill GG (Eds); Berlin, Heidelberg: Springer: 61-69.
PUB | DOI
Comparison of cluster algorithms for the analysis of text data using Kolmogorov complexity.
In: ICONIP 2008. Köppen M, Kasabov NK, Coghill GG (Eds); Berlin, Heidelberg: Springer: 61-69.
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993848
Rossi F, Hasenfuß A, Hammer B (2007)
Accelerating Relational Clustering Algorithms With Sparse Prototype Representation.
In: Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007). Bielefeld: Bielefeld University.
PUB
| PDF | DOI
Accelerating Relational Clustering Algorithms With Sparse Prototype Representation.
In: Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007). Bielefeld: Bielefeld University.
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994016
Schneider P, Biehl M, Schleif F-M, Hammer B (2007)
Advanced metric adaptation in Generalized LVQ for classification of mass spectrometry data.
In: Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007). Bielefeld: Bielefeld University.
PUB
| PDF | DOI
Advanced metric adaptation in Generalized LVQ for classification of mass spectrometry data.
In: Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007). Bielefeld: Bielefeld University.
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994267
Villmann T, Schleif F-M, Merenyi E, Strickert M, Hammer B (2007)
Class imaging of hyperspectral satellite remote sensing data using FLSOM.
In: Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007). Bielefeld: Bielefeld University.
PUB
| PDF | DOI
Class imaging of hyperspectral satellite remote sensing data using FLSOM.
In: Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007). Bielefeld: Bielefeld University.
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994295
Witoelar A, Biehl M, Hammer B (2007)
Learning Vector Quantization: generalization ability and dynamics of competing prototypes.
In: Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007). Bielefeld: Bielefeld University.
PUB
| PDF | DOI
Learning Vector Quantization: generalization ability and dynamics of competing prototypes.
In: Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007). Bielefeld: Bielefeld University.
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993782
Hasenfuss A, Hammer B (2007)
Relational topographic maps.
In: Advances in Intelligent Data Analysis VII, Proceedings of the 7th International Symposium on Intelligent Data Analysis. Berthold MR, Shawe-Taylor J, Lavrac N (Eds);4723. Berlin: Springer: 93-105.
PUB | DOI
Relational topographic maps.
In: Advances in Intelligent Data Analysis VII, Proceedings of the 7th International Symposium on Intelligent Data Analysis. Berthold MR, Shawe-Taylor J, Lavrac N (Eds);4723. Berlin: Springer: 93-105.
2007 | Report | Veröffentlicht | PUB-ID: 1993922
Schleif F-M, Hasenfuss A, Hammer B (2007)
Aggregation of multiple peak lists by use of an improved neural gas network.
Leipzig: Universität Leipzig.
PUB
Aggregation of multiple peak lists by use of an improved neural gas network.
Leipzig: Universität Leipzig.
2007 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993297
Biehl M, Ghosh A, Hammer B (2007)
Dynamics and generalization ability of LVQ algorithms.
Journal of Machine Learning Research 8: 323-360.
PUB
Dynamics and generalization ability of LVQ algorithms.
Journal of Machine Learning Research 8: 323-360.
2007 | Report | Veröffentlicht | PUB-ID: 1993533
Hammer B, Hasenfuss A (2007)
Relational topographic Maps. IfI Technical reports.
Clausthal-Zellerfeld: Clausthal University of Technology.
PUB
Relational topographic Maps. IfI Technical reports.
Clausthal-Zellerfeld: Clausthal University of Technology.
2007 | Report | Veröffentlicht | PUB-ID: 1993831
Melato M, Hammer B, Hormann K (2007)
Neural Gas for Surface Reconstruction. IfI Technical reports.
Clausthal-Zellerfeld: Clausthal University of Technology.
PUB
Neural Gas for Surface Reconstruction. IfI Technical reports.
Clausthal-Zellerfeld: Clausthal University of Technology.
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993970
Schleif F-M, Villmann T, Hammer B (2007)
Analysis of Proteomic Spectral Data by Multi Resolution Analysis and Self-Organizing-Maps.
In: Application of Fuzzy Sets Theory. Proceedings of the 7th International Workshop on Fuzzy Logic and Applications. LNAI 4578. Masulli F, Mitra S, Pasi G (Eds); Berlin, Heidelberg: Springer: 563-570.
PUB | DOI
Analysis of Proteomic Spectral Data by Multi Resolution Analysis and Self-Organizing-Maps.
In: Application of Fuzzy Sets Theory. Proceedings of the 7th International Workshop on Fuzzy Logic and Applications. LNAI 4578. Masulli F, Mitra S, Pasi G (Eds); Berlin, Heidelberg: Springer: 563-570.
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993999
Schneider P, Biehl M, Hammer B (2007)
Relevance matrices in LVQ.
In: Proc. Of European Symposium on Artificial Neural Networks. Verleysen M (Ed); Brussels, Belgium: d-side publications: 37-42.
PUB
Relevance matrices in LVQ.
In: Proc. Of European Symposium on Artificial Neural Networks. Verleysen M (Ed); Brussels, Belgium: d-side publications: 37-42.
2007 | Report | Veröffentlicht | PUB-ID: 1993334
Blazewicz J, Ecker K, Hammer B (2007)
ICOLE-2007, German-Polish Workshop on Computational Biology, Scheduling and Machine Learning. Lessach, Austria, 27.05.-02.06.2007.
Clausthal-Zellerfeld: Clausthal University of Technology.
PUB
ICOLE-2007, German-Polish Workshop on Computational Biology, Scheduling and Machine Learning. Lessach, Austria, 27.05.-02.06.2007.
Clausthal-Zellerfeld: Clausthal University of Technology.
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993746
Hammer B, Villmann T (2007)
How to process uncertainty in machine learning.
In: Proc. Of European Symposium on Artificial Neural Networks (ESANN'2007). Verleysen M (Ed); Brussels, Belgium: d-side publications: 79-90.
PUB
How to process uncertainty in machine learning.
In: Proc. Of European Symposium on Artificial Neural Networks (ESANN'2007). Verleysen M (Ed); Brussels, Belgium: d-side publications: 79-90.
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993811
Hasenfuss A, Hammer B, Schleif F-M, Villmann T (2007)
Neural gas clustering for dissimilarity data with continuous prototypes.
In: Computational and Ambient Intelligence – Proceedings of the 9th Work-conference on Artificial Neural Networks. LNCS 4507. Sandoval F, Prieto A, Cabestany J, Grana M (Eds); Berlin: Springer: 539-546.
PUB | DOI
Neural gas clustering for dissimilarity data with continuous prototypes.
In: Computational and Ambient Intelligence – Proceedings of the 9th Work-conference on Artificial Neural Networks. LNCS 4507. Sandoval F, Prieto A, Cabestany J, Grana M (Eds); Berlin: Springer: 539-546.
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994299
Witolaer A, Biehl M, Ghosh A, Hammer B (2007)
On the dynamics of vector quantization and neural gas.
In: Proc. Of European Symposium on Artificial Neural Networks (ESANN'2007). Verleysen M (Ed); Brussels, Belgium: d-side publications: 127-132.
PUB
On the dynamics of vector quantization and neural gas.
In: Proc. Of European Symposium on Artificial Neural Networks (ESANN'2007). Verleysen M (Ed); Brussels, Belgium: d-side publications: 127-132.
2007 | Konferenzband | Veröffentlicht | PUB-ID: 1994321
Biehl M, Hammer B, Verleysen M, Villmann T (Eds) (2007)
Similarity-based Clustering and its Application to Medicine and Biology.; 7131.
Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI).
PUB
Similarity-based Clustering and its Application to Medicine and Biology.; 7131.
Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI).
2007 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1993630
Hammer B, Micheli A, Sperduti A (2007)
Adaptive Contextual Processing of Structured Data by Recursive Neural Networks: A Survey of Computational Properties.
In: Perspectives of Neural-Symbolic Integration. Hammer B, Hitzler P (Eds); Studies in computational Intelligence, 77, Berlin: Springer: 67-94.
PUB | DOI
Adaptive Contextual Processing of Structured Data by Recursive Neural Networks: A Survey of Computational Properties.
In: Perspectives of Neural-Symbolic Integration. Hammer B, Hitzler P (Eds); Studies in computational Intelligence, 77, Berlin: Springer: 67-94.
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993820
Hasenfuss A, Hammer B, Schleif F-M, Villmann T (2007)
Neural gas clustering for sparse proximity data.
In: Proceedings of the 9th International Work-Conference on Artificial Neural Networks.LNCS 4507. Sandoval F, Prieto A, Cabestany J, Grana M (Eds); Berlin, Heidelberg, Germany: Springer: 539-546.
PUB
Neural gas clustering for sparse proximity data.
In: Proceedings of the 9th International Work-Conference on Artificial Neural Networks.LNCS 4507. Sandoval F, Prieto A, Cabestany J, Grana M (Eds); Berlin, Heidelberg, Germany: Springer: 539-546.
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993907
Schleif F-M, Hammer B, Villmann T (2007)
Supervised Neural Gas for Functional Data and its Application to the Analysis of Clinical Proteom Spectra.
In: Computational and Ambient Intelligence. Proceedings of the 9th International Work-Conference on Artificial Neural Networks. LNCS, 4507. Sandoval F, Prieto A, Cabestany J, Grana M (Eds); Berlin, Heidelberg: Springer: 1036-1044.
PUB | DOI
Supervised Neural Gas for Functional Data and its Application to the Analysis of Clinical Proteom Spectra.
In: Computational and Ambient Intelligence. Proceedings of the 9th International Work-Conference on Artificial Neural Networks. LNCS, 4507. Sandoval F, Prieto A, Cabestany J, Grana M (Eds); Berlin, Heidelberg: Springer: 1036-1044.
2007 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1994102
Tino P, Hammer B, Boden M (2007)
Markovian Bias of Neural-based Architectures With Feedback Connections.
In: Perspectives of Neural-Symbolic Integration. Hammer B, Hitzler P (Eds); Studies in computational Intelligence, 77, Berlin: Springer: 95-134.
PUB | DOI
Markovian Bias of Neural-based Architectures With Feedback Connections.
In: Perspectives of Neural-Symbolic Integration. Hammer B, Hitzler P (Eds); Studies in computational Intelligence, 77, Berlin: Springer: 95-134.
2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994258
Villmann T, Schleif F-M, Merenyi E, Hammer B (2007)
Fuzzy Labeled Self Organizing Map for Clasification of Spectra.
In: Computational and Ambient Intelligence. Proceedings of the 9th Work-conference on Artificial Neural Networks. LNCS, 4507. Sandoval F, Prieto A, Cabestany J, Grana M (Eds); Berlin: Springer: 556-563.
PUB | DOI
Fuzzy Labeled Self Organizing Map for Clasification of Spectra.
In: Computational and Ambient Intelligence. Proceedings of the 9th Work-conference on Artificial Neural Networks. LNCS, 4507. Sandoval F, Prieto A, Cabestany J, Grana M (Eds); Berlin: Springer: 556-563.
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993895
Schleif F-M, Hammer B, Villmann T (2006)
Margin based Active Learning for LVQ Networks.
In: Proc. Of European Symposium on Artificial Neural Networks. Verleysen M (Ed); Brussels, Belgium: d-side publications: 539-544.
PUB
Margin based Active Learning for LVQ Networks.
In: Proc. Of European Symposium on Artificial Neural Networks. Verleysen M (Ed); Brussels, Belgium: d-side publications: 539-544.
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994184
Villmann T, Hammer B, Schleif F-M, Geweniger T, Fischer T, Cottrell M (2006)
Prototype based classification using information theoretic learning.
In: Neural Information Processing, 13th International Conference. Proceedings. King I, Wang J, Chan L, Wang DLL (Eds); Lecture Notes in Computer Science, 4233, Part II. Berlin: Springer: 40-49.
PUB
Prototype based classification using information theoretic learning.
In: Neural Information Processing, 13th International Conference. Proceedings. King I, Wang J, Chan L, Wang DLL (Eds); Lecture Notes in Computer Science, 4233, Part II. Berlin: Springer: 40-49.
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994273
Villmann T, Seiffert U, Schleif F-M, Brüß C, Geweniger T, Hammer B (2006)
Fuzzy Labeled Self-Organizing Map with Label-Adjusted Prototypes.
In: Proceedings of Conference Artificial Neural Networks in Pattern Recognition. Schwenker F (Ed); Berlin: Springer: 46-56.
PUB | DOI
Fuzzy Labeled Self-Organizing Map with Label-Adjusted Prototypes.
In: Proceedings of Conference Artificial Neural Networks in Pattern Recognition. Schwenker F (Ed); Berlin: Springer: 46-56.
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993889
Schleif F-M, Elssner T, Kostrzewa M, Villmann T, Hammer B (2006)
Machine Learning and Soft-Computing in Bioinformatics. A Short Journey.
In: Proc. of FLINS 2006. World Scientific Press: 541-548.
PUB
Machine Learning and Soft-Computing in Bioinformatics. A Short Journey.
In: Proc. of FLINS 2006. World Scientific Press: 541-548.
2006 | Report | Veröffentlicht | PUB-ID: 1993322
Biehl M, Hammer B, Schneider P (2006)
Matrix Learning in Learning Vector Quantization.
Clausthal-Zellerfeld: Clausthal University of Technology.
PUB
Matrix Learning in Learning Vector Quantization.
Clausthal-Zellerfeld: Clausthal University of Technology.
2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993391
Cottrell M, Hammer B, Hasenfuss A, Villmann T (2006)
Batch and Median Neural Gas.
Neural Networks 19(6-7): 762-771.
PUB | DOI | WoS | PubMed | Europe PMC
Batch and Median Neural Gas.
Neural Networks 19(6-7): 762-771.
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993568
Hammer B, Hasenfuss A, Schleif F-M, Villmann T (2006)
Supervised median neural gas.
In: Smart Engineering System Design. Intelligent Engineering Systems Through Artificial Neural Networks, 16. Dagli C, Buczak A, Enke D, Embrechts A, Ersoy O (Eds); ASME Press: 623-633.
PUB
Supervised median neural gas.
In: Smart Engineering System Design. Intelligent Engineering Systems Through Artificial Neural Networks, 16. Dagli C, Buczak A, Enke D, Embrechts A, Ersoy O (Eds); ASME Press: 623-633.
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993594
Hammer B, Hasenfuss A, Schleif F-M, Villmann T (2006)
Supervised median clustering.
In: Smart systems engineering : infra-structure systems engineering, bio-informatics and computational biology and evolutionary computation : proceedings of the Artificial Neural Networks in Engineering Conference (ANNIE 2006). Dagli CH (Ed); ASME Press series on intelligent engineering systems through artificial neural networks, 16, New York, NY: ASME Press: 623-632.
PUB
Supervised median clustering.
In: Smart systems engineering : infra-structure systems engineering, bio-informatics and computational biology and evolutionary computation : proceedings of the Artificial Neural Networks in Engineering Conference (ANNIE 2006). Dagli CH (Ed); ASME Press series on intelligent engineering systems through artificial neural networks, 16, New York, NY: ASME Press: 623-632.
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993878
Schleif F-M, Elssner T, Kostrzewa M, Villmann T, Hammer B (2006)
Analysis and Visualization of Proteomic Data by Fuzzy labeled Self-Organizing Maps.
In: 19th IEEE International Symposium on Computer- based Medical Systems. Lee DJ, Nutter B, Antani S, Mitra S, Archibald J (Eds); Los Alamitos: IEEE Computer Society Press: 919-924.
PUB | DOI
Analysis and Visualization of Proteomic Data by Fuzzy labeled Self-Organizing Maps.
In: 19th IEEE International Symposium on Computer- based Medical Systems. Lee DJ, Nutter B, Antani S, Mitra S, Archibald J (Eds); Los Alamitos: IEEE Computer Society Press: 919-924.
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994028
Seiffert U, Hammer B, Kaski S, Villmann T (2006)
Neural Networks and Machine Learning in Bioinformatics - Theory and Applications.
In: Proc. Of European Symposium on Artificial Neural Networks. Verleysen M (Ed); Brussels, Belgium: d-side publications: 521-532.
PUB
Neural Networks and Machine Learning in Bioinformatics - Theory and Applications.
In: Proc. Of European Symposium on Artificial Neural Networks. Verleysen M (Ed); Brussels, Belgium: d-side publications: 521-532.
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994201
Villmann T, Hammer B, Seiffert U (2006)
Perspectives of Self-adapted Self-organizing Clustering in Organic Computing.
In: Biologically Inspired Approaches to Advanced Information Technology, Second International Workshop. Proceedings. Lecture Notes in Computer Science, 3853. Ijspeert AJ, Masuzawa T, Kusumoto S (Eds); Berlin: Springer: 141-159.
PUB | DOI
Perspectives of Self-adapted Self-organizing Clustering in Organic Computing.
In: Biologically Inspired Approaches to Advanced Information Technology, Second International Workshop. Proceedings. Lecture Notes in Computer Science, 3853. Ijspeert AJ, Masuzawa T, Kusumoto S (Eds); Berlin: Springer: 141-159.
2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994237
Villmann T, Schleif F-M, Hammer B (2006)
Comparison of relevance learning vector quantization with other metric adaptive classification methods.
Neural Networks 19(5): 610-622.
PUB | DOI | WoS | PubMed | Europe PMC
Comparison of relevance learning vector quantization with other metric adaptive classification methods.
Neural Networks 19(5): 610-622.
2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993440
Ghosh A, Biehl M, Hammer B (2006)
Performance analysis of LVQ algorithms: a statistical physics approach.
Neural Networks 19(6-7): 817-829.
PUB | DOI | WoS | PubMed | Europe PMC
Performance analysis of LVQ algorithms: a statistical physics approach.
Neural Networks 19(6-7): 817-829.
2006 | Report | Veröffentlicht | PUB-ID: 1993584
Hammer B, Hasenfuss A, Schleif F-M, Villmann T (2006)
Supervised median clustering. IfI Technical reports.
Clausthal-Zellerfeld: Clausthal University of Technology.
PUB
Supervised median clustering. IfI Technical reports.
Clausthal-Zellerfeld: Clausthal University of Technology.
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993611
Hammer B, Hasenfuss A, Villmann T (2006)
Magnification Control for Batch Neural Gas.
In: Proc. Of European Symposium on Artificial Neural Networks. Verleysen M (Ed); Brussels: d-side publications: 7-12.
PUB
Magnification Control for Batch Neural Gas.
In: Proc. Of European Symposium on Artificial Neural Networks. Verleysen M (Ed); Brussels: d-side publications: 7-12.
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993659
Hammer B, Neubauer N (2006)
On the capacity of unsupervised recursive neural networks for symbol processing.
In: Workshop proceedings of NeSy'06. d'Avila Garcez A, Hitzler P, Tamburrini G (Eds);.
PUB
On the capacity of unsupervised recursive neural networks for symbol processing.
In: Workshop proceedings of NeSy'06. d'Avila Garcez A, Hitzler P, Tamburrini G (Eds);.
2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993762
Hammer B, Villmann T (2006)
Effizient Klassifizieren und Clustern: Lernparadigmen von Vektorquantisierern.
Künstliche Intelligenz 3(6): 5-11.
PUB
Effizient Klassifizieren und Clustern: Lernparadigmen von Vektorquantisierern.
Künstliche Intelligenz 3(6): 5-11.
2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994195
Villmann T, Hammer B, Schleif F-M, Geweniger T, Herrmann W (2006)
Fuzzy Classification by Fuzzy Labeled Neural Gas.
Neural Networks 19(6-7): 772-779.
PUB | DOI | WoS | PubMed | Europe PMC
Fuzzy Classification by Fuzzy Labeled Neural Gas.
Neural Networks 19(6-7): 772-779.
2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2017225
Hammer B, Villmann T, Schleif F-M, Albani C, Hermann W (2006)
Learning vector quantization classification with local relevance determination for medical data.
In: Artificial Intelligence and Soft-Computing - Proceedings of ICAISC 2006. LNAI, 4029. Rutkowski L, Tadeusiewicz R, Zadeh LA, Zurada J (Eds); Lecture notes in computer science ; 4029 : Lecture notes in artificial intelligence, 4029. Berlin, Heidelberg: Springer: 603-612.
PUB | DOI
Learning vector quantization classification with local relevance determination for medical data.
In: Artificial Intelligence and Soft-Computing - Proceedings of ICAISC 2006. LNAI, 4029. Rutkowski L, Tadeusiewicz R, Zadeh LA, Zurada J (Eds); Lecture notes in computer science ; 4029 : Lecture notes in artificial intelligence, 4029. Berlin, Heidelberg: Springer: 603-612.
2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993624
Hammer B, Micheli A, Neubauer N, Sperduti A, Strickert M (2005)
Self Organizing Maps for Time Series.
In: Proceedings of WSOM 2005. 115-122.
PUB
Self Organizing Maps for Time Series.
In: Proceedings of WSOM 2005. 115-122.
2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994172
Villmann T, Hammer B, Schleif F-M, Geweniger T (2005)
Fuzzy Labeled Neural GAS for Fuzzy Classification.
In: Proceedings of the 5th Workshop on Self-Organizing Maps [on CD-ROM]. Cottrell M (Ed); Paris, France: University Paris-1-Pantheon-Sorbonne: 283-290.
PUB
Fuzzy Labeled Neural GAS for Fuzzy Classification.
In: Proceedings of the 5th Workshop on Self-Organizing Maps [on CD-ROM]. Cottrell M (Ed); Paris, France: University Paris-1-Pantheon-Sorbonne: 283-290.
2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993305
Biehl M, Gosh A, Hammer B (2005)
The dynamics of Learning Vector Quantization.
In: ESANN'05. Verleysen M (Ed); Evere: d-side publishing: 13-18.
PUB
The dynamics of Learning Vector Quantization.
In: ESANN'05. Verleysen M (Ed); Evere: d-side publishing: 13-18.
2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993386
Cottrell M, Hammer B, Hasenfuss A, Villmann T (2005)
Batch NG.
In: Proceedings of WSOM 2005. 275-282.
PUB
Batch NG.
In: Proceedings of WSOM 2005. 275-282.
2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993444
Ghosh A, Biehl M, Hammer B (2005)
Dynamical Analysis of LVQ type learning rules.
In: Proceedings of WSOM. 578-594.
PUB
Dynamical Analysis of LVQ type learning rules.
In: Proceedings of WSOM. 578-594.
2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993665
Hammer B, Rechtien A, Strickert M, Villmann V (2005)
Relevance learning for mental disease classification.
In: ESANN'05. Verleysen M (Ed); d-side publishing: 139-144.
PUB
Relevance learning for mental disease classification.
In: ESANN'05. Verleysen M (Ed); d-side publishing: 139-144.
2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994118
Tluk von Toschanowitz K, Hammer B, Ritter H (2005)
Relevance determination in reinforcement learning.
In: ESANN'05. Verleysen M (Ed); d-side publishing: 369-374.
PUB
Relevance determination in reinforcement learning.
In: ESANN'05. Verleysen M (Ed); d-side publishing: 369-374.
2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994219
Villmann T, Schleif F-M, Hammer B (2005)
Fuzzy Classification for Classification of Mass Spectrometric Data Based on Learning Vector Quantization.
In: International Workshop on Integrative Bioinformatics. .
PUB
Fuzzy Classification for Classification of Mass Spectrometric Data Based on Learning Vector Quantization.
In: International Workshop on Integrative Bioinformatics. .
2005 | Report | Veröffentlicht | PUB-ID: 1993675
Hammer B, Schleif F-M, Villmann T (2005)
On the Generalization Ability of Prototype-Based Classifiers with Local Relevance Determination. IfI Technical reports.
Clausthal-Zellerfeld: Clausthal University of Technology.
PUB
On the Generalization Ability of Prototype-Based Classifiers with Local Relevance Determination. IfI Technical reports.
Clausthal-Zellerfeld: Clausthal University of Technology.
2005 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993671
Hammer B, Saunders C, Sperduti A (2005)
Special issue on neural networks and kernel methods for structured domains.
Neural Networks 18(8): 1015-1018.
PUB | DOI | WoS | PubMed | Europe PMC
Special issue on neural networks and kernel methods for structured domains.
Neural Networks 18(8): 1015-1018.
2005 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1993710
Hammer B, Strickert M, Villmann T (2005)
Prototype based recognition of splice sites.
In: Bioinformatics using computational intelligence paradigms. Seiffert U, Jain LC, Schweitzer P (Eds); Berlin: Springer: 25-55.
PUB
Prototype based recognition of splice sites.
In: Bioinformatics using computational intelligence paradigms. Seiffert U, Jain LC, Schweitzer P (Eds); Berlin: Springer: 25-55.
2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993974
Schleif F-M, Villmann T, Hammer B (2005)
Local Metric Adaptation for Soft Nearest Prototype Classification to Classify Proteomic Data.
In: Proceedings of the 6th Workshop on Fuzzy Logic and Applications. Bloch I, Petrosino A, Tettamanzi AGB (Eds); Berlin, Heidelberg: Springer: 290-296.
PUB | DOI
Local Metric Adaptation for Soft Nearest Prototype Classification to Classify Proteomic Data.
In: Proceedings of the 6th Workshop on Fuzzy Logic and Applications. Bloch I, Petrosino A, Tettamanzi AGB (Eds); Berlin, Heidelberg: Springer: 290-296.
2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994249
Villmann T, Schleif F-M, Hammer B (2005)
Fuzzy labeled soft nearest neighbor classification with relevance learning.
In: Proceedings of the International Conference of Machine Learning Applications. Wani MA, Cios KJ, Hafeez K (Eds); Los Angeles: IEEE Press: 11-15.
PUB
Fuzzy labeled soft nearest neighbor classification with relevance learning.
In: Proceedings of the International Conference of Machine Learning Applications. Wani MA, Cios KJ, Hafeez K (Eds); Los Angeles: IEEE Press: 11-15.
2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993750
Hammer B, Villmann T (2005)
Classification using non standard metrics.
In: ESANN'05. Verleysen M (Ed); Brussels: d-side publishing: 303-316.
PUB
Classification using non standard metrics.
In: ESANN'05. Verleysen M (Ed); Brussels: d-side publishing: 303-316.
2004 | Report | Veröffentlicht | PUB-ID: 1993732
Hammer B, Tino P, Micheli A (2004)
A mathematical characterization of the architectural bias of recursive models. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
PUB
A mathematical characterization of the architectural bias of recursive models. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994168
Villmann T, Hammer B, Schleif F-M (2004)
Metrik Adaptation for Optimal Feature Classification in Learning Vector Quantization Applied to Environment Detection.
In: Proceedings of Selbstorganisation Von Adaptivem Verfahren. Fortschritts-Berichte VDI Reihe 10, Nr. 742. Groß H-M, Debes K, Böhme H-J (Eds); VDI Verlag: 592-597.
PUB
Metrik Adaptation for Optimal Feature Classification in Learning Vector Quantization Applied to Environment Detection.
In: Proceedings of Selbstorganisation Von Adaptivem Verfahren. Fortschritts-Berichte VDI Reihe 10, Nr. 742. Groß H-M, Debes K, Böhme H-J (Eds); VDI Verlag: 592-597.
2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994111
Tluk von Toschanowitz K, Hammer B, Ritter H (2004)
Mapping the Design Space of Reinforcement Learning Problems - a Case Study.
In: SOAVE 2004, 3rd Workshop on SelfOrganization of AdaptiVE Behavior. Gross H-M, Debes K, Böhme H-J (Eds); VDI Verlag: 251-261.
PUB
Mapping the Design Space of Reinforcement Learning Problems - a Case Study.
In: SOAVE 2004, 3rd Workshop on SelfOrganization of AdaptiVE Behavior. Gross H-M, Debes K, Böhme H-J (Eds); VDI Verlag: 251-261.
2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994212
Villmann T, Schleif F-M, Hammer B (2004)
Metric adaptation for optimal feature classification in learning vector quantization applied to environment detection.
In: SOAVE 2004, 3rd Workshop on SelfOrganization of AdaptiVE Behavior. Groß H-M, Debes K, Böhme H-J (Eds); VDI Verlag.
PUB
Metric adaptation for optimal feature classification in learning vector quantization applied to environment detection.
In: SOAVE 2004, 3rd Workshop on SelfOrganization of AdaptiVE Behavior. Groß H-M, Debes K, Böhme H-J (Eds); VDI Verlag.
2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993620
Hammer B, Jain BJ (2004)
Neural methods for non-standard data.
In: European Symposium on Artificial Neural Networks'2004. Verleysen M (Ed); D-side publications: 281-292.
PUB
Neural methods for non-standard data.
In: European Symposium on Artificial Neural Networks'2004. Verleysen M (Ed); D-side publications: 281-292.
2004 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993649
Hammer B, Micheli A, Sperduti A, Strickert M (2004)
Recursive self-organizing network models.
Neural Networks 17(8-9): 1061-1085.
PUB | DOI | WoS | PubMed | Europe PMC
Recursive self-organizing network models.
Neural Networks 17(8-9): 1061-1085.
2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993702
Hammer B, Strickert M, Villmann T (2004)
Relevance LVQ versus SVM.
In: Artificial Intelligence and Softcomputing, Lecture Notes in Artificial Intelligence, 3070. Rutkowski L, Siekmann J, Tadeusiewicz R, Zadeh LA (Eds); Berlin: Springer: 592-597.
PUB
Relevance LVQ versus SVM.
In: Artificial Intelligence and Softcomputing, Lecture Notes in Artificial Intelligence, 3070. Rutkowski L, Siekmann J, Tadeusiewicz R, Zadeh LA (Eds); Berlin: Springer: 592-597.
2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993419
Gersmann K, Hammer B (2004)
A reinforcement learning algorithm to improve scheduling search heuristics with the SVM.
In: IJCNN. .
PUB
A reinforcement learning algorithm to improve scheduling search heuristics with the SVM.
In: IJCNN. .
2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993870
Schleif F-M, Clauss U, Villmann T, Hammer B (2004)
Supervised Relevance Neural Gas and Unified Maximum Separability Analysis for Classification of Mass Spectrometric Data.
In: Proceedings of the 3rd International Conference on Machine Learning and Applications (ICMLA) 2004. Wani MA, Cios KJ, Hafeez K (Eds); Los Alamitos, CA, USA: IEEE Press: 374-379.
PUB
Supervised Relevance Neural Gas and Unified Maximum Separability Analysis for Classification of Mass Spectrometric Data.
In: Proceedings of the 3rd International Conference on Machine Learning and Applications (ICMLA) 2004. Wani MA, Cios KJ, Hafeez K (Eds); Los Alamitos, CA, USA: IEEE Press: 374-379.
2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994049
Strickert M, Hammer B (2004)
Self-organizing context learning.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); D-side publications: 39-44.
PUB
Self-organizing context learning.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); D-side publications: 39-44.
2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994099
Tino P, Hammer B (2004)
On early stages of learning in connectionist models with feedback connections.
In: Compositional Connectionism in Cognitive Science. .
PUB
On early stages of learning in connectionist models with feedback connections.
In: Compositional Connectionism in Cognitive Science. .
2003 | Report | Veröffentlicht | PUB-ID: 1993725
Hammer B, Strickert M, Villmann T (2003)
On the generalization ability of GRLVQ. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
PUB
On the generalization ability of GRLVQ. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
2003 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994108
Tiño P, Hammer B (2003)
Architectural Bias in Recurrent Neural Networks: Fractal Analysis.
Neural Computation 15(8): 1931-1957.
PUB
Architectural Bias in Recurrent Neural Networks: Fractal Analysis.
Neural Computation 15(8): 1931-1957.
2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994223
Villmann T, Schleif F-M, Hammer B (2003)
Supervised Neural Gas and Relevance Learning in Learning Vector Quantization.
In: Proceedings of the 4th Workshop on Self Organizing Maps [on CD-ROM]. Yamakawa T (Ed); Hibikino, Kitakyushu, Japan: Kyushu Institute of Technology: 47-52.
PUB
Supervised Neural Gas and Relevance Learning in Learning Vector Quantization.
In: Proceedings of the 4th Workshop on Self Organizing Maps [on CD-ROM]. Yamakawa T (Ed); Hibikino, Kitakyushu, Japan: Kyushu Institute of Technology: 47-52.
2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993338
Bojer T, Hammer B, Koeers C (2003)
Monitoring technical systems with prototype based clustering.
In: ESANN 2003, 10th European Symposium on Artificial Neural Network. Proceedings. Verleysen M (Ed); Evere: D-side publication: 433-439.
PUB
Monitoring technical systems with prototype based clustering.
In: ESANN 2003, 10th European Symposium on Artificial Neural Network. Proceedings. Verleysen M (Ed); Evere: D-side publication: 433-439.
2003 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993530
Hammer B, Gersmann K (2003)
A Note on the Universal Approximation Capability of Support Vector Machines.
Neural Processing Letters 17(1): 43-53.
PUB
A Note on the Universal Approximation Capability of Support Vector Machines.
Neural Processing Letters 17(1): 43-53.
2003 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1993487
Hammer B (2003)
Perspectives on learning symbolic data with connectionistic systems.
In: Adaptivity and Learning. Kühn R, Menzel R, Menzel W, Ratsch U, Richter MM, Stamatescu I (Eds); Berlin: Springer: 141-160.
PUB
Perspectives on learning symbolic data with connectionistic systems.
In: Adaptivity and Learning. Kühn R, Menzel R, Menzel W, Ratsch U, Richter MM, Stamatescu I (Eds); Berlin: Springer: 141-160.
2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993754
Hammer B, Villmann T (2003)
Mathematical Aspects of Neural Networks.
In: Proc. Of European Symposium on Artificial Neural Networks (ESANN'2003). Verleysen M (Ed); Brussels, Belgium: d-side: 59-72.
PUB
Mathematical Aspects of Neural Networks.
In: Proc. Of European Symposium on Artificial Neural Networks (ESANN'2003). Verleysen M (Ed); Brussels, Belgium: d-side: 59-72.
2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994053
Strickert M, Hammer B (2003)
Unsupervised recursive sequence processing.
In: 10th European Symposium on Artificial Neural Networks. Proceedings. Verleysen M (Ed); D-side publication: 27-32.
PUB
Unsupervised recursive sequence processing.
In: 10th European Symposium on Artificial Neural Networks. Proceedings. Verleysen M (Ed); D-side publication: 27-32.
2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994060
Strickert M, Hammer B (2003)
Neural Gas for Sequences.
In: WSOM'03. 53-57.
PUB
Neural Gas for Sequences.
In: WSOM'03. 53-57.
2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993412
Gersmann K, Hammer B (2003)
Improving iterative repair strategies for scheduling with the SVM.
In: ESANN 2003, 10th European Symposium on Artificial Neural Networks. Proceedings. Verleysen M (Ed); Evere: D-side publication: 235-240.
PUB
Improving iterative repair strategies for scheduling with the SVM.
In: ESANN 2003, 10th European Symposium on Artificial Neural Networks. Proceedings. Verleysen M (Ed); Evere: D-side publication: 235-240.
2003 | Report | Veröffentlicht | PUB-ID: 1993645
Hammer B, Micheli a., Sperduti A (2003)
A general framework for self-organizing structure processing neural networks.
Pisa: Universita di Pisa, Dipartimento die Informatica.
PUB
A general framework for self-organizing structure processing neural networks.
Pisa: Universita di Pisa, Dipartimento die Informatica.
2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993349
Bojer T, Hammer B, Strickert M, Villmann T (2003)
Determining Relevant Input Dimensions for the Self-Organizing Map.
In: Neural Networks and Soft Computing (Proc. ICNNSC 2002). Rutkowski L, Kacprzyk J (Eds); Physica-Verlag: 388-393.
PUB
Determining Relevant Input Dimensions for the Self-Organizing Map.
In: Neural Networks and Soft Computing (Proc. ICNNSC 2002). Rutkowski L, Kacprzyk J (Eds); Physica-Verlag: 388-393.
2003 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993736
Hammer B, Tiño P (2003)
Recurrent Neural Networks with Small Weights Implement Definite Memory Machines.
Neural Computation 15(8): 1897-1929.
PUB
Recurrent Neural Networks with Small Weights Implement Definite Memory Machines.
Neural Computation 15(8): 1897-1929.
2003 | Report | Veröffentlicht | PUB-ID: 1994157
Villmann T, Hammer B (2003)
Metric adaptation and relevance learning in learning vector quantization. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
PUB
Metric adaptation and relevance learning in learning vector quantization. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
2003 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994208
Villmann T, Merényi E, Hammer B (2003)
Neural maps in remote sensing image analysis.
Neural Networks 16(3-4): 389-403.
PUB
Neural maps in remote sensing image analysis.
Neural Networks 16(3-4): 389-403.
2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993636
Hammer B, Micheli A, Sperduti A (2002)
A general framework for unsupervised processing of structured data.
In: ESANN 2002, 10th European Symposium on Artificial Neural Network. Proceedings. Verleysen M (Ed); De-side publication: 389-394.
PUB
A general framework for unsupervised processing of structured data.
In: ESANN 2002, 10th European Symposium on Artificial Neural Network. Proceedings. Verleysen M (Ed); De-side publication: 389-394.
2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994095
Tino P, Hammer B (2002)
Architectural bias in recurrent neural networks – fractal analysis.
In: Proc. International Conf. on Artificial Neural Networks. Lecture Notes in Computer Science, 2415. Dorronsoro JR (Ed); Berlin: Springer: 370-376.
PUB
Architectural bias in recurrent neural networks – fractal analysis.
In: Proc. International Conf. on Artificial Neural Networks. Lecture Notes in Computer Science, 2415. Dorronsoro JR (Ed); Berlin: Springer: 370-376.
2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994146
Villmann T, Hammer B (2002)
Supervised Neural Gas for Learning Vector Quantization.
In: Proc. of the 5th German Workshop on Artificial Life. Polani D, Kim J, Martinetz T (Eds); Berlin: Akademische Verlagsgesellschaft - infix - IOS Press: 9-16.
PUB
Supervised Neural Gas for Learning Vector Quantization.
In: Proc. of the 5th German Workshop on Artificial Life. Polani D, Kim J, Martinetz T (Eds); Berlin: Akademische Verlagsgesellschaft - infix - IOS Press: 9-16.
2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993688
Hammer B, Steil JJ (2002)
Perspectives on Learning with Recurrent Neural Networks.
In: Proc. European Symposium Artificial Neural Networks. Verleysen M (Ed); D-side publication: 357-368.
PUB
Perspectives on Learning with Recurrent Neural Networks.
In: Proc. European Symposium Artificial Neural Networks. Verleysen M (Ed); D-side publication: 357-368.
2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993758
Hammer B, Villmann T (2002)
Batch-GRLVQ.
In: Proc. Of European Symposium on Artificial Neural Networks (ESANN'2002). Verleysen M (Ed); Brussels, Belgium: d-side: 295-300.
PUB
Batch-GRLVQ.
In: Proc. Of European Symposium on Artificial Neural Networks (ESANN'2002). Verleysen M (Ed); Brussels, Belgium: d-side: 295-300.
2002 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993765
Hammer B, Villmann T (2002)
Generalized Relevance Learning Vector Quantization.
Neural Networks 15(8-9): 1059-1068.
PUB
Generalized Relevance Learning Vector Quantization.
Neural Networks 15(8-9): 1059-1068.
2002 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1993471
Hammer B (2002)
Compositionality in Neural Systems.
In: Handbook of Brain Theory and Neural Networks. Arbib M (Ed); 2nd. MIT Press: 244-248.
PUB
Compositionality in Neural Systems.
In: Handbook of Brain Theory and Neural Networks. Arbib M (Ed); 2nd. MIT Press: 244-248.
2002 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993508
Hammer B (2002)
Recurrent neural networks for structured data – a unifying approach and its properties.
Cognitive Systems Research 3(2): 145-165.
PUB
Recurrent neural networks for structured data – a unifying approach and its properties.
Cognitive Systems Research 3(2): 145-165.
2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993692
Hammer B, Strickert M, Villmann T (2002)
Learning Vector Quantization for Multimodal Data.
In: Proc. International Conf. on Artificial Neural Networks (ICANN). Lecture Notes in Computer Science, 2415. Dorronsoro JR (Ed); Berlin: Springer Verlag: 370-376.
PUB
Learning Vector Quantization for Multimodal Data.
In: Proc. International Conf. on Artificial Neural Networks (ICANN). Lecture Notes in Computer Science, 2415. Dorronsoro JR (Ed); Berlin: Springer Verlag: 370-376.
2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993697
Hammer B, Strickert M, Villmann T (2002)
Rule Extraction from Self-Organizing Networks.
In: Proc. International Conf. on Artificial Neural Networks (ICANN). Lecture Notes in Computer Science, 2415. Dorronsoro JR (Ed); Berlin: Springer Verlag: 877-883.
PUB
Rule Extraction from Self-Organizing Networks.
In: Proc. International Conf. on Artificial Neural Networks (ICANN). Lecture Notes in Computer Science, 2415. Dorronsoro JR (Ed); Berlin: Springer Verlag: 877-883.
2002 | Report | Veröffentlicht | PUB-ID: 1993729
Hammer B, Tino P (2002)
Neural networks with small weights implement finite memory machines. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
PUB
Neural networks with small weights implement finite memory machines. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
2001 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993768
Hammer B, Villmann T (2001)
Input Pruning for Neural Gas Architectures.
In: Proc. Of European Symposium on Artificial Neural Networks (ESANN'2001). Brussels, Belgium: D facto publications: 283-288.
PUB
Input Pruning for Neural Gas Architectures.
In: Proc. Of European Symposium on Artificial Neural Networks (ESANN'2001). Brussels, Belgium: D facto publications: 283-288.
2001 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993343
Bojer T, Hammer B, Schunk D, Tluk von Toschanowitz K (2001)
Relevance determination in learning vector quantization.
In: ESANN'2001. Verleysen M (Ed); D-facto publications: 271-276.
PUB
Relevance determination in learning vector quantization.
In: ESANN'2001. Verleysen M (Ed); D-facto publications: 271-276.
2001 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994123
Vidyasagar M, Balaji S, Hammer B (2001)
Closure properties of uniform convergence of empirical means and PAC learnability under a family of probability measures.
System and Control Letters 42: 151-157.
PUB
Closure properties of uniform convergence of empirical means and PAC learnability under a family of probability measures.
System and Control Letters 42: 151-157.
2001 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993474
Hammer B (2001)
On the Generalization Ability of Recurrent Networks.
In: Artificial Neural Networks. Proceedings. Lecture Notes in Computer Science, 2130. Dorffner G, Bischof H, Hornik K (Eds); Berlin: Springer: 731-736.
PUB
On the Generalization Ability of Recurrent Networks.
In: Artificial Neural Networks. Proceedings. Lecture Notes in Computer Science, 2130. Dorffner G, Bischof H, Hornik K (Eds); Berlin: Springer: 731-736.
2001 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993739
Hammer B, Villmann T (2001)
Estimating Relevant Input Dimensions for Self-Organizing Algorithms.
In: Advances in Self-Organising Maps. Allinson NM, Yin H, Allinson L, Slack J (Eds); London: Springer: 173-180.
PUB
Estimating Relevant Input Dimensions for Self-Organizing Algorithms.
In: Advances in Self-Organising Maps. Allinson NM, Yin H, Allinson L, Slack J (Eds); London: Springer: 173-180.
2001 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994042
Strickert M, Bojer T, Hammer B (2001)
Generalized Relevance LVQ for Time Series.
In: Artificial Neural Networks. International Conference. Proceedings. Lecture Notes in Computer Science, 2130. Dorffner G, Bischof H, Hornik K (Eds); Berlin: Springer: 677-683.
PUB
Generalized Relevance LVQ for Time Series.
In: Artificial Neural Networks. International Conference. Proceedings. Lecture Notes in Computer Science, 2130. Dorffner G, Bischof H, Hornik K (Eds); Berlin: Springer: 677-683.
2001 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993510
Hammer B (2001)
Generalization Ability of Folding Networks.
IEEE Trans. Knowl. Data Eng. 13(2): 196-206.
PUB
Generalization Ability of Folding Networks.
IEEE Trans. Knowl. Data Eng. 13(2): 196-206.
2000 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993499
Hammer B (2000)
Limitations of hybrid systems.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); D-facto publications: 213-218.
PUB
Limitations of hybrid systems.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); D-facto publications: 213-218.
2000 | Monographie | Veröffentlicht | PUB-ID: 1993514
Hammer B (2000)
Learning with Recurrent Neural Networks. Lecture Notes in Control and Information Sciences, 254.
Berlin: Springer.
PUB
Learning with Recurrent Neural Networks. Lecture Notes in Control and Information Sciences, 254.
Berlin: Springer.
2000 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993512
Hammer B (2000)
On the approximation capability of recurrent neural networks.
Neurocomputing 31(1-4): 107-123.
PUB
On the approximation capability of recurrent neural networks.
Neurocomputing 31(1-4): 107-123.
2000 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993400
DasGupta B, Hammer B (2000)
On Approximate Learning by Multi-layered Feedforward Circuits.
In: Algorithmic Learning Theory, 11th International Conference. Proceedings. Lecture Notes in Computer Science, 1968. Arimura H, Jain S, Sharma A (Eds); Berlin: Springer: 264-278.
PUB
On Approximate Learning by Multi-layered Feedforward Circuits.
In: Algorithmic Learning Theory, 11th International Conference. Proceedings. Lecture Notes in Computer Science, 1968. Arimura H, Jain S, Sharma A (Eds); Berlin: Springer: 264-278.
2000 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993479
Hammer B (2000)
Approximation and generalization issues of recurrent networks dealing with structured data.
In: ECAI workshop: Foundations of connectionist-symbolic integration: representation, paradigms, and algorithms. Frasconi P, Sperduti A, Gori M (Eds);.
PUB
Approximation and generalization issues of recurrent networks dealing with structured data.
In: ECAI workshop: Foundations of connectionist-symbolic integration: representation, paradigms, and algorithms. Frasconi P, Sperduti A, Gori M (Eds);.
2000 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993495
Hammer B (2000)
Neural networks classifying symbolic data.
In: ICML workshop on attribute-value and relational learning: crossing the boundaries. de Raedt L, Kramer S (Eds); 61-65.
PUB
Neural networks classifying symbolic data.
In: ICML workshop on attribute-value and relational learning: crossing the boundaries. de Raedt L, Kramer S (Eds); 61-65.
1999 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993516
Hammer B (1999)
On the learnability of recursive data.
Mathematics of Control, Signals and Systems 12: 62-79.
PUB
On the learnability of recursive data.
Mathematics of Control, Signals and Systems 12: 62-79.
1999 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993502
Hammer B (1999)
Approximation capabilities of folding networks.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); D-facto publications: 33-38.
PUB
Approximation capabilities of folding networks.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); D-facto publications: 33-38.
1999 | Report | Veröffentlicht | PUB-ID: 1993409
DasGupta B, Hammer B (1999)
Hardness of approximation of the loading problem for multi-layered feedforward neural networks.
DIMACS Center, Rutgers University.
PUB
Hardness of approximation of the loading problem for multi-layered feedforward neural networks.
DIMACS Center, Rutgers University.
1998 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993484
Hammer B (1998)
On the Approximation Capability of Recurrent Neural Networks.
In: Proceedings of the International ICSC / IFAC Symposium on Neural Computation (NC 1998). Heiss M (Ed); ICSC Academic Press: 512-518.
PUB
On the Approximation Capability of Recurrent Neural Networks.
In: Proceedings of the International ICSC / IFAC Symposium on Neural Computation (NC 1998). Heiss M (Ed); ICSC Academic Press: 512-518.
1998 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993505
Hammer B (1998)
Training a sigmoidal network is difficult.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); D-facto publications: 255-260.
PUB
Training a sigmoidal network is difficult.
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); D-facto publications: 255-260.
1998 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993518
Hammer B (1998)
Some complexity results for perceptron networks.
In: International Conference on artificial Neural Networks. 639-644.
PUB
Some complexity results for perceptron networks.
In: International Conference on artificial Neural Networks. 639-644.
1997 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993526
Hammer B (1997)
Generalization of Elman Networks.
In: Artificial Neural Networks - ICANN '97, 7th International Conference. Proceedings. Lecture Notes in Computer Science, 1327. Berlin: Springer: 409-414.
PUB
Generalization of Elman Networks.
In: Artificial Neural Networks - ICANN '97, 7th International Conference. Proceedings. Lecture Notes in Computer Science, 1327. Berlin: Springer: 409-414.
1997 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993684
Hammer B, Sperschneider V (1997)
Neural networks can approximate mappings on structured objects.
In: International conference on Computational Intelligence and Neural Networks. Wang PP (Ed); 211-214.
PUB
Neural networks can approximate mappings on structured objects.
In: International conference on Computational Intelligence and Neural Networks. Wang PP (Ed); 211-214.
1997 | Report | Veröffentlicht | PUB-ID: 1993524
Hammer B (1997)
On the generalization ability of simple recurrent neural networks. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
PUB
On the generalization ability of simple recurrent neural networks. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
1997 | Report | Veröffentlicht | PUB-ID: 1993520
Hammer B (1997)
Learning recursive data is intractable. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
PUB
Learning recursive data is intractable. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
1997 | Report | Veröffentlicht | PUB-ID: 1993522
Hammer B (1997)
A NP-hardness result for a sigmoidal 3-node neural network. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
PUB
A NP-hardness result for a sigmoidal 3-node neural network. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
1996 | Report | Veröffentlicht | PUB-ID: 1993528
Hammer B (1996)
Universal approximation of mappings on structured objects using the folding architecture. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
PUB
Universal approximation of mappings on structured objects using the folding architecture. Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
1996 | Monographie | Veröffentlicht | PUB-ID: 1994039
Sperschneider V, Hammer B (1996)
Theoretische Informatik. Eine problemorientierte Einführung.
erlin: Springer.
PUB
Theoretische Informatik. Eine problemorientierte Einführung.
erlin: Springer.