Barbara Hammer

bhammer@techfak.uni-bielefeld.de
ORCID logo https://orcid.org/0000-0002-0935-5591

PEVZ-ID
18302458

551 Publikationen

Alle markieren

  • [551]
    2025 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 3001606
    R. Feldhans and B. Hammer, “Towards Reliable Drift Detection and Explanation in Text Data”, Intelligent Data Engineering and Automated Learning – IDEAL 2024, PT I, Lecture Notes in Computer Science, vol. 15346, Cham: Springer , 2025, pp.301-312.
    PUB | DOI | WoS
     
  • [550]
    2025 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 3001658
    H. Peters, et al., “Development of a digital twin for data-driven modeling of punch-bending processes using a graphical modeling notation”, at - Automatisierungstechnik, vol. 73, 2025, pp. 173-184.
    PUB | DOI | WoS
     
  • [549]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2989164
    R. Velioglu, R.K.-W. Chan, and B. Hammer, “FashionFail: Addressing Failure Cases in Fashion Object Detection and Segmentation”, 2024 International Joint Conference on Neural Networks (IJCNN), IEEE International Joint Conference on Neural Networks (IJCNN), New York: Institute of Electrical and Electronics Engineers (IEEE), 2024.
    PUB | DOI | WoS | arXiv
     
  • [548]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 3001615
    T. Markmann, M. Straat, and B. Hammer, “Koopman-Based Surrogate Modelling of Turbulent Rayleigh-Benard Convection”, 2024 International Joint Conference on Neural Networks (IJCNN), IEEE International Joint Conference on Neural Networks (IJCNN), New York: Institute of Electrical and Electronics Engineers (IEEE), 2024.
    PUB | DOI | WoS
     
  • [547]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 3001626
    C. Internò, et al., “Federated Loss Exploration for Improved Convergence on Non-IID Data”, 2024 International Joint Conference on Neural Networks (IJCNN), IEEE International Joint Conference on Neural Networks (IJCNN), New York: Institute of Electrical and Electronics Engineers (IEEE), 2024.
    PUB | DOI | WoS
     
  • [546]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2993740
    V. Vaquet, et al., “Localizing of Anomalies in Critical Infrastructure using Model-Based Drift Explanations”, 2024 International Joint Conference on Neural Networks (IJCNN), IEEE, 2024, pp.1-8.
    PUB | DOI | WoS
     
  • [545]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2992441
    S. Schroeder, A. Schulz, and B. Hammer, “The SAME score: Improved cosine based measure for semantic bias”, 2024 International Joint Conference on Neural Networks (IJCNN), IEEE, 2024, pp.1-8.
    PUB | DOI | WoS
     
  • [544]
    2024 | Konferenzbeitrag | PUB-ID: 3000176
    F. Fumagalli, et al., “KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions”, Proceedings of the 41st International Conference on Machine Learning, R. Salakhutdinov, et al., eds., Proceedings of Machine Learning Research, vol. 235, PMLR, 2024, pp.14308-14342.
    PUB
     
  • [543]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2993714 OA
    A. Mazur, et al., “Visualizing and Improving 3D Mesh Segmentation with DeepView”, ESANN 2024 proceedings, Louvain-la-Neuve (Belgium): Ciaco - i6doc.com, 2024, pp.649-654.
    PUB | PDF | DOI | Download (ext.)
     
  • [542]
    2024 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2993739
    V. Vaquet, et al., “Challenges, Methods, Data–A Survey of Machine Learning in Water Distribution Networks”, Artificial Neural Networks and Machine Learning – ICANN 2024. 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17–20, 2024, Proceedings, Part IX, M. Wand, et al., eds., Lecture Notes in Computer Science, Cham: Springer Nature Switzerland, 2024, pp.155-170.
    PUB | DOI | WoS
     
  • [541]
    2024 | Preprint | PUB-ID: 2999454
    R. Velioglu, et al., “TryOffDiff: Virtual-Try-Off via High-Fidelity Garment Reconstruction using Diffusion Models”, arXiv:2411.18350, 2024.
    PUB | arXiv
     
  • [540]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2994141
    M. Vieth, et al., “Similarity-Based Zero-Shot Domain Adaptation for Wearables”, ESANN 2024 proceedings, Louvain-la-Neuve (Belgium): Ciaco - i6doc.com, 2024, pp.221-226.
    PUB | DOI
     
  • [539]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2994158
    B. Hammer, “Explaining Neural Networks - Deep and Shallow”, Advances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and Beyond (WSOM+ 2024) , T. Villmann, et al., eds., Lecture Notes in Networks and Systems, vol. 1087, Cham: Springer , 2024, pp.139-140.
    PUB | DOI | WoS
     
  • [538]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2991394
    P. Kolpaczki, et al., “SVARM-IQ: Efficient Approximation of Any-order Shapley Interactions through Stratification”, International Conference on Artificial Intelligence and Statistics, 2-4 May 2024, Palau de Congressos, Valencia, Spain, S. Dasgupta, S. Mandt, and Y. Li, eds., Proceedings of Machine Learning Research, vol. 238, San Diego: PMLR, 2024, pp.3520-3528.
    PUB | WoS | arXiv
     
  • [537]
    2024 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2994227
    A. Artelt, et al., “EPyT-Flow: A Toolkit for Generating Water DistributionNetwork Data”, Journal of Open Source Software, vol. 9, 2024, : 7104.
    PUB | DOI
     
  • [536]
    2024 | Konferenzbeitrag | PUB-ID: 2992095
    F. Störck, et al., “FairGLVQ: Fairness in Partition-Based Classification”, Proceedings of the 15th International Workshop on Self-Organizing Maps (WSOM 2024), T. Villmann, et al., eds., Cham: Springer Nature Switzerland, 2024, pp.141-151.
    PUB | DOI | Download (ext.) | WoS
     
  • [535]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2993721
    H. Peters, et al., “Novel approach for data-driven modelling of multi-stage straightening and bending processes”, Material Forming: ESAFORM 2024, Materials Research Proceedings, vol. 41, Materials Research Forum LLC, 2024, pp.2289-2298.
    PUB | DOI
     
  • [534]
    2024 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2993652
    Z.H. Shah, et al., “Image restoration in frequency space using complex-valued CNNs”, Frontiers in Artificial Intelligence , vol. 7, 2024, : 1353873.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [533]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2993852
    A. Liuliakov, et al., “Noise Robust One-Class Intrusion Detection on Dynamic Graphs”, ESANN 2024 proceedings, Louvain-la-Neuve (Belgium): Ciaco - i6doc.com, 2024, pp.363-368.
    PUB | DOI
     
  • [532]
    2024 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2991468 OA
    F. Hinder, V. Vaquet, and B. Hammer, “One or two things we know about concept drift—a survey on monitoring in evolving environments. Part B: locating and explaining concept drift”, Frontiers in Artificial Intelligence, vol. 7, 2024.
    PUB | PDF | DOI | WoS | PubMed | Europe PMC
     
  • [531]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2993744
    V. Vaquet, et al., “Self-Supervised Learning from Incrementally Drifting Data Streams”, ESANN 2024 proceesdings, Louvain-la-Neuve (Belgium): Ciaco - i6doc.com, 2024, pp.431-436.
    PUB | DOI
     
  • [530]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2993743
    F. Hinder, V. Vaquet, and B. Hammer, “On the Fine Structure of Drifting Features”, ESANN 2024 proceesdings, Louvain-la-Neuve (Belgium): Ciaco - i6doc.com, 2024, pp.63-68.
    PUB | DOI
     
  • [529]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2993741
    J. Brinkrolf, et al., “Causes of Rejects in Prototype-based Classification Aleatoric vs. Epistemic Uncertainty”, ESANN 2024 proceesdings, Louvain-la-Neuve (Belgium): Ciaco - i6doc.com, 2024, pp.191-196.
    PUB | DOI
     
  • [528]
    2024 | Report | Veröffentlicht | PUB-ID: 2992602 OA
    B. Hammer, et al., Sustainable Life-Cycle of Intelligent Socio-Technical Systems, 2024.
    PUB | Dateien verfügbar | DOI
     
  • [527]
    2024 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2988509
    F. Hinder, V. Vaquet, and B. Hammer, “A Remark on Concept Drift for Dependent Data”, Advances in Intelligent Data Analysis XXII. 22nd International Symposium on Intelligent Data Analysis, IDA 2024, Stockholm, Sweden, April 24–26, 2024, Proceedings, Part I, I. Miliou, N. Piatkowski, and P. Papapetrou, eds., Lecture Notes in Computer Science, Cham: Springer Nature Switzerland, 2024, pp.77-89.
    PUB | DOI | WoS
     
  • [526]
    2024 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2992700
    J. Strotherm, et al., “Fairness in KI-Systemen”, Vertrauen in Künstliche Intelligenz. Eine multi-perspektivische Betrachtung, S. Schork, ed., Wiesbaden: Springer Fachmedien Wiesbaden, 2024, pp.163-183.
    PUB | DOI | arXiv
     
  • [525]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2992337 OA
    D. Zanutto, et al., “A Water Futures Approach on Water Demand Forecasting with Online Ensemble Learning”, The 3rd International Joint Conference on Water Distribution Systems Analysis & Computing and Control for the Water Industry (WDSA/CCWI 2024), Basel Switzerland: MDPI, 2024, pp.60.
    PUB | PDF | DOI
     
  • [524]
    2024 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2991444
    F. Hinder, V. Vaquet, and B. Hammer, “Feature-based analyses of concept drift”, Neurocomputing, vol. 600, 2024, : 127968.
    PUB | DOI | WoS
     
  • [523]
    2024 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2991310 OA
    F. Hinder, V. Vaquet, and B. Hammer, “One or two things we know about concept drift-a survey on monitoring in evolving environments. Part A: detecting concept drift”, Frontiers in Artificial Intelligence , vol. 7, 2024, : 1330257.
    PUB | PDF | DOI | WoS | PubMed | Europe PMC
     
  • [522]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2987572
    S. Schroeder, et al., “Semantic Properties of Cosine Based Bias Scores for Word Embeddings”, Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods. Vol. 1, Setúbal, Portugal: SCITEPRESS - Science and Technology Publications, 2024, pp.160-168.
    PUB | DOI
     
  • [521]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2987573
    N. Grimmelsmann, et al., “Predicting the Level of Co-Activation of One Muscle Head from the Other Muscle Head of the Biceps Brachii Muscle by Linear Regression and Shallow Feedforward Neural Networks”, Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies, Setúbal, Portugal: SCITEPRESS - Science and Technology Publications, 2024, pp.611-621.
    PUB | DOI
     
  • [520]
    2024 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2988165
    M. Muschalik, et al., “Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles”, Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, 2024, pp. 14388-14396.
    PUB | DOI
     
  • [519]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2988098
    V. Vaquet, F. Hinder, and B. Hammer, “Investigating the Suitability of Concept Drift Detection for Detecting Leakages in Water Distribution Networks”, Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods ICPRAM. Volume 1, SCITEPRESS - Science and Technology Publications, 2024, pp.296-303.
    PUB | DOI
     
  • [518]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2984049
    M.I. Ashraf, et al., “Spatial Graph Convolution Neural Networks for Water Distribution Systems”, Advances in Intelligent Data Analysis XXI. 21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-la-Neuve, Belgium, April 12–14, 2023, Proceedings, B. Crémilleux, S. Hess, and S. Nijssen, eds., Lecture Notes in Computer Science, Cham: Springer Nature Switzerland, 2023, pp.29-41.
    PUB | DOI
     
  • [517]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2983942
    M. Muschalik, et al., “iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios”, Explainable Artificial Intelligence. First World Conference, xAI 2023, Lisbon, Portugal, July 26–28, 2023, Proceedings, Part I, L. Longo, ed., Communications in Computer and Information Science, Cham: Springer Nature Switzerland, 2023, pp.177-194.
    PUB | DOI | WoS
     
  • [516]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2983795
    U. Kuhl, A. Artelt, and B. Hammer, “For Better or Worse: The Impact of Counterfactual Explanations’ Directionality on User Behavior in xAI”, Explainable Artificial Intelligence. First World Conference, xAI 2023, Lisbon, Portugal, July 26–28, 2023, Proceedings, Part III, L. Longo, ed., Communications in Computer and Information Science, Cham: Springer Nature Switzerland, 2023, pp.280-300.
    PUB | DOI | WoS
     
  • [515]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2987580
    F. Fumagalli, et al., “SHAP-IQ: Unified Approximation of any-order Shapley Interactions”, Advances in Neural Information Processing Systems 36 (NeurIPS 2023), Advances in Neural Information Processing Systems, 2023.
    PUB | Download (ext.) | WoS | arXiv
     
  • [514]
    2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2980429 OA
    J. Kummert, A. Schulz, and B. Hammer, “Metric Learning with Self-Adjusting Memory for Explaining Feature Drift”, SN Computer Science, vol. 4, 2023, : 376.
    PUB | PDF | DOI
     
  • [513]
    2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2979703
    A. Liuliakov, L. Hermes, and B. Hammer, “AutoML technologies for the identification of sparse classification and outlier detection models”, Applied Soft Computing, vol. 133, 2023, : 109942.
    PUB | DOI | WoS
     
  • [512]
    2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2979026
    J. Jakob, et al., “Interpretable SAM-kNN Regressor for Incremental Learning on High-Dimensional Data Streams”, Applied Artificial Intelligence, vol. 37, 2023, : 2198846.
    PUB | DOI | WoS
     
  • [511]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2985684
    J. Kummert, et al., “Generating Cardiovascular Data to Improve Training of Assistive Heart Devices”, 2023 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, 2023, pp.1292-1297.
    PUB | DOI
     
  • [510]
    2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2969734 OA
    U. Kuhl, A. Artelt, and B. Hammer, “Let's go to the Alien Zoo: Introducing an experimental framework to study usability of counterfactual explanations for machine learning”, Frontiers in Computer Science, vol. 5, 2023, : 1087929.
    PUB | PDF | DOI | Download (ext.) | WoS | arXiv
     
  • [509]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2985683
    R. Feldhans, et al., “Data Augmentation for Cardiovascular Time Series Data Using WaveNet”, 2023 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, 2023, pp.836-841.
    PUB | DOI
     
  • [508]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2980971
    J. Strotherm and B. Hammer, “Fairness-Enhancing Ensemble Classification in Water Distribution Networks”, Presented at the International Work-Conference on Artificial Neural Networks (IWANN) 2023, Ponta Delgada, 2023.
    PUB | DOI | Download (ext.)
     
  • [507]
    2023 | Konferenzbeitrag | Angenommen | PUB-ID: 2982899 OA
    V. Vaquet, J. Brinkrolf, and B. Hammer, “Robust Feature Selection and Robust Training to Cope with Hyperspectral Sensor Shifts”, Accepted.
    PUB | PDF
     
  • [506]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2977934
    F. Hinder, et al., “On the Change of Decision Boundary and Loss in Learning with Concept Drift”, Advances in Intelligent Data Analysis XXI. 21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-la-Neuve, Belgium, April 12–14, 2023, Proceedings, B. Crémilleux, S. Hess, and S. Nijssen, eds., Lecture Notes in Computer Science, vol. 13876, Cham: Springer , 2023, pp.182-194.
    PUB | DOI
     
  • [505]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982167
    F. Hinder, et al., “On the Hardness and Necessity of Supervised Concept Drift Detection”, Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods ICPRAM. Vol. 1, M. De Marsico, G. Sanniti di Baja, and A. Fred, eds., Setúbal: SCITEPRESS - Science and Technology Publications, 2023, pp.164-175.
    PUB | DOI
     
  • [504]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969381
    S. Schroeder, et al., “So Can We Use Intrinsic Bias Measures or Not?”, Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods, Setúbal, Portugal: SCITEPRESS - Science and Technology Publications, 2023, pp.403-410.
    PUB | DOI
     
  • [503]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969382
    P. Kenneweg, et al., “Debiasing Sentence Embedders Through Contrastive Word Pairs”, Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods, Setúbal, Portugal: SCITEPRESS - Science and Technology Publications, 2023, pp.205-212.
    PUB | DOI
     
  • [502]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969383
    A. Artelt, A. Schulz, and B. Hammer, “"Why Here and not There?": Diverse Contrasting Explanations of Dimensionality Reduction”, Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods, Setúbal, Portugal: SCITEPRESS - Science and Technology Publications, 2023, pp.27-38.
    PUB | DOI | arXiv
     
  • [501]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2983756
    F. Fumagalli, et al., “On Feature Removal for Explainability in Dynamic Environments”, ESANN 2023 proceedings, 2023, pp.83-88.
    PUB | DOI
     
  • [500]
    2023 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2983727
    F. Fumagalli, et al., “Incremental permutation feature importance (iPFI): towards online explanations on data streams”, Machine Learning , 2023.
    PUB | DOI | WoS
     
  • [499]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2983250
    M. Vieth, A. Schulz, and B. Hammer, “Extending Drift Detection Methods to Identify When Exactly the Change Happened”, Advances in Computational Intelligence. 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Ponta Delgada, Portugal, June 19–21, 2023, Proceedings, Part I, I. Rojas, G. Joya, and A. Catala, eds., Lecture Notes in Computer Science, Cham: Springer Nature Switzerland, 2023, pp.92-104.
    PUB | DOI
     
  • [498]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2983455
    A. Liuliakov, et al., “One-Class Intrusion Detection with Dynamic Graphs”, Artificial Neural Networks and Machine Learning – ICANN 2023. 32nd International Conference on Artificial Neural Networks, Heraklion, Crete, Greece, September 26–29, 2023, Proceedings, Part IV, L. Iliadis, et al., eds., Lecture Notes in Computer Science, Cham: Springer Nature Switzerland, 2023, pp.537-549.
    PUB | DOI
     
  • [497]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2983943
    M. Muschalik, et al., “iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams”, Machine Learning and Knowledge Discovery in Databases: Research Track. European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part III, D. Koutra, et al., eds., Lecture Notes in Computer Science, Cham: Springer Nature Switzerland, 2023, pp.428-445.
    PUB | DOI
     
  • [496]
    2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2981289
    F. Hinder, et al., “Model-based explanations of concept drift”, Neurocomputing, 2023, : 126640.
    PUB | DOI | Download (ext.) | WoS
     
  • [495]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2985571
    A. Artelt, et al., “Unsupervised Unlearning of Concept Drift with Autoencoders”, 2023 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, 2023, pp.703-710.
    PUB | DOI
     
  • [494]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982830
    F. Hinder and B. Hammer, “Feature Selection for Concept Drift Detection”, ESANN 2023 Proceedings, M. Verleysen, ed., 2023.
    PUB
     
  • [493]
    2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2983759
    P. Koundouri, et al., “Behavioral Economics and Neuroeconomics of Environmental Values”, Annual Review of Resource Economics, vol. 15, 2023, pp. 153-176.
    PUB | DOI | Download (ext.) | WoS
     
  • [492]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2984048
    C. Schulte-Schüren, et al., “Best of both, Structured and Unstructured Sparsity in Neural Networks”, Proceedings of the 3rd Workshop on Machine Learning and Systems, New York, NY, USA: ACM, 2023, pp.104-108.
    PUB | DOI
     
  • [491]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2984047
    P. Kenneweg, et al., “Faster Convergence for Transformer Fine-tuning with Line Search Methods”, 2023 International Joint Conference on Neural Networks (IJCNN), IEEE, 2023, pp.1-8.
    PUB | DOI
     
  • [490]
    2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2983728
    A. Artelt, R. Visser, and B. Hammer, “"I do not know! but why?"- Local model-agnostic example-based explanations of reject”, Neurocomputing, vol. 558, 2023, : 126722.
    PUB | DOI | WoS
     
  • [489]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2983457
    S. Schroeder, et al., “Measuring Fairness with Biased Data: A Case Study on the Effects of Unsupervised Data in Fairness Evaluation”, Advances in Computational Intelligence. 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Ponta Delgada, Portugal, June 19–21, 2023, Proceedings, Part I, I. Rojas, G. Joya, and A. Catala, eds., Lecture Notes in Computer Science, Cham: Springer Nature Switzerland, 2023, pp.134-145.
    PUB | DOI
     
  • [488]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2983406
    P. Stahlhofen, et al., “Adversarial Attacks on Leakage Detectors in Water Distribution Networks”, Advances in Computational Intelligence. 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Ponta Delgada, Portugal, June 19–21, 2023, Proceedings, Part II, I. Rojas, G. Joya, and A. Catala, eds., Lecture Notes in Computer Science, Cham: Springer Nature Switzerland, 2023, pp.451-463.
    PUB | DOI | Preprint
     
  • [487]
    2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2978162 OA
    D. Stallmann and B. Hammer, “Unsupervised Cyclic Siamese Networks Automating Cell Imagery Analysis”, Algorithms, vol. 16, 2023, : 205.
    PUB | PDF | DOI | WoS
     
  • [486]
    2023 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2968921
    M. Schilling, et al., “Modularity in Nervous Systems-a Key to Efficient Adaptivity for Deep Reinforcement Learning”, Cognitive Computation, 2023.
    PUB | DOI | WoS
     
  • [485]
    2022 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2962746 OA
    A. Artelt, et al., “Contrasting Explanations for Understanding and Regularizing Model Adaptations”, Neural Processing Letters, vol. 55, 2022, pp. 5273–5297.
    PUB | PDF | DOI | Download (ext.) | WoS
     
  • [484]
    2022 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2967683 OA
    P. Kenneweg, D. Stallmann, and B. Hammer, “Novel transfer learning schemes based on Siamese networks and synthetic data”, Neural Computing and Applications, vol. 35, 2022, pp. 8423–8436.
    PUB | PDF | DOI | Download (ext.) | WoS | PubMed | Europe PMC
     
  • [483]
    2022 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982135
    J. Jakob, M. Hasenjäger, and B. Hammer, “Reject Options for Incremental Regression Scenarios”, Artificial Neural Networks and Machine Learning – ICANN 2022. 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings; Part IV, E. Pimenidis, et al., eds., Lecture Notes in Computer Science, Cham: Springer Nature Switzerland, 2022, pp.248-259.
    PUB | DOI
     
  • [482]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969736
    U. Kuhl, A. Artelt, and B. Hammer, “Keep Your Friends Close and Your Counterfactuals Closer: Improved Learning From Closest Rather Than Plausible Counterfactual Explanations in an Abstract Setting”, 2022 ACM Conference on Fairness, Accountability, and Transparency, New York, NY, USA: ACM, 2022, pp.2125-2137.
    PUB | DOI | Download (ext.)
     
  • [481]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969235
    A. Castellani, S. Schmitt, and B. Hammer, “Stream-Based Active Learning with Verification Latency in Non-stationary Environments”, Artificial Neural Networks and Machine Learning – ICANN 2022. 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings; Part IV, E. Pimenidis, et al., eds., Lecture Notes in Computer Science, vol. 13532, Cham: Springer Nature Switzerland, 2022, pp.260-272.
    PUB | DOI | Download (ext.)
     
  • [480]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969461
    A. Artelt and B. Hammer, ““Even if …” – Diverse Semifactual Explanations of Reject”, 2022 IEEE Symposium Series on Computational Intelligence (SSCI), H. Ishibuchi, ed., Piscataway, NJ: IEEE, 2022, pp.854-859.
    PUB | DOI
     
  • [479]
    2022 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2967296
    R. Velioglu, et al., “Explainable Artificial Intelligence for Improved Modeling of Processes”, Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings, H. Yin, D. Camacho, and P. Tino, eds., Lecture Notes in Computer Science, vol. 13756, Cham: Springer International Publishing, 2022, pp.313-325.
    PUB | DOI
     
  • [478]
    2022 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2969459
    J. Jakob, et al., “SAM-kNN Regressor for Online Learning in Water Distribution Networks”, Artificial Neural Networks and Machine Learning – ICANN 2022. 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings, Part III, E. Pimenidis, et al., eds., Lecture Notes in Computer Science, vol. 13531, Cham: Springer Nature , 2022, pp.752-762.
    PUB | DOI
     
  • [477]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969460
    A. Artelt, et al., “Explaining Reject Options of Learning Vector Quantization Classifiers”, Proceedings of the 14th International Joint Conference on Computational Intelligence, SCITEPRESS - Science and Technology Publications, 2022, pp.249-261.
    PUB | DOI
     
  • [476]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2966088
    F. Hinder, et al., “Localization of Concept Drift: Identifying the Drifting Datapoints”, 2022 International Joint Conference on Neural Networks (IJCNN), IEEE, 2022, pp.1-9.
    PUB | DOI | Download (ext.)
     
  • [475]
    2022 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2967410
    M. Vieth, et al., “Efficient Sensor Selection for Individualized Prediction Based on Biosignals”, Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings, H. Yin, D. Camacho, and P. Tino, eds., Lecture Notes in Computer Science, vol. 13756, Cham: Springer International Publishing, 2022, pp.326-337.
    PUB | DOI | Download (ext.)
     
  • [474]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2966600
    P. Kenneweg, S. Schroeder, and B. Hammer, “Neural Architecture Search for Sentence Classification with BERT”, ESANN 2022 proceedings, Louvain-la-Neuve (Belgium): Ciaco - i6doc.com, 2022, pp.417-422.
    PUB | DOI
     
  • [473]
    2022 | Konferenzbeitrag | Angenommen | PUB-ID: 2964534
    V. Vaquet, et al., “Federated learning vector quantization for dealing with drift between nodes”, Presented at the 30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2022, Bruges, Accepted.
    PUB
     
  • [472]
    2022 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2964421
    M. Muschalik, et al., “Agnostic Explanation of Model Change based on Feature Importance”, KI - Künstliche Intelligenz, 2022.
    PUB | DOI | Download (ext.) | WoS
     
  • [471]
    2022 | Report | Veröffentlicht | PUB-ID: 2965286
    A. Artelt, et al., Faire Algorithmen und die Fairness von Erklärungen: Informatische, rechtliche und ethische Perspektiven, DuEPublico: Duisburg-Essen Publications online, University of Duisburg-Essen, Germany, 2022.
    PUB | DOI | Download (ext.)
     
  • [470]
    2022 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2962928
    V. Vaquet, et al., “Investigating Intensity and Transversal Drift in Hyperspectral Imaging Data”, Neurocomputing, 2022.
    PUB | DOI | WoS
     
  • [469]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2962650 OA
    V. Vaquet, et al., “Taking care of our drinking water: Dealing with Sensor Faults in Water Distribution Networks”, Presented at the 31st International Conference on Artificial Neural Networks, Bristol, 2022.
    PUB | PDF
     
  • [468]
    2022 | Kurzbeitrag Konferenz / Poster | PUB-ID: 2962861
    F. Hinder, et al., “Localization of Concept Drift: Identifying the Drifting Datapoints”, 2022.
    PUB
     
  • [467]
    2022 | Preprint | PUB-ID: 2962919 OA
    A. Artelt, et al., “One Explanation to Rule them All — Ensemble Consistent Explanations”, ArXiv:2205.08974 , 2022.
    PUB | PDF | Download (ext.) | arXiv
     
  • [466]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2967096
    P. Kenneweg, et al., “Intelligent Learning Rate Distribution to Reduce Catastrophic Forgetting in Transformers”, Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings, H. Yin, D. Camacho, and P. Tino, eds., Lecture Notes in Computer Science, vol. 13756, Cham: Springer International Publishing, 2022, pp.252-261.
    PUB | DOI
     
  • [465]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2987492
    D. Savic, et al., “Long-Term Transitioning of Water Distribution Systems: ERC Water-Futures Project”, Proceedings - 2nd International Join Conference on Water Distribution System Analysis (WDSA)& Computing and Control in the Water Industry (CCWI), València: Editorial Universitat Politècnica de València, 2022.
    PUB | DOI
     
  • [464]
    2022 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2984050
    F. Hinder, V. Vaquet, and B. Hammer, “Suitability of Different Metric Choices for Concept Drift Detection”, Advances in Intelligent Data Analysis XX. 20th International Symposium on Intelligent Data Analysis, IDA 2022, Rennes, France, April 20–22, 2022, Proceedings, T. Bouadi, E. Fromont, and E. Hüllermeier, eds., Lecture Notes in Computer Science, Cham: Springer International Publishing, 2022, pp.157-170.
    PUB | DOI
     
  • [463]
    2022 | Zeitschriftenaufsatz | PUB-ID: 2978998
    B. Paaßen, et al., “Reservoir Memory Machines as Neural Computers”, IEEE Transactions on Neural Networks and Learning Systems, vol. 33, 2022, pp. 2575–2585.
    PUB | DOI | Download (ext.) | arXiv
     
  • [462]
    2022 | Report | Veröffentlicht | PUB-ID: 2965622 OA
    B. Hammer, et al., Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse von Daten mittels Maschinellen Lernens, Bielefeld: Univ. Bielefeld, Forschungsinstitut für Kognition und Robotik, 2022.
    PUB | PDF | DOI
     
  • [461]
    2022 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2964829
    L. Langnickel, et al., “BERT WEAVER: Using WEight AVERaging to Enable Lifelong Learning for Transformer-based Models”, arXiv, 2022.
    PUB | DOI | arXiv
     
  • [460]
    2022 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2961873
    J.P. Göpfert, H. Wersing, and B. Hammer, “Interpretable locally adaptive nearest neighbors”, Neurocomputing, vol. 470, 2022, pp. 344-351.
    PUB | DOI | WoS
     
  • [459]
    2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2958662
    M. Schilling, et al., “Decentralized control and local information for robust and adaptive decentralized Deep Reinforcement Learning”, Neural Networks, vol. 144, 2021, pp. 699-725.
    PUB | DOI | Download (ext.) | WoS | PubMed | Europe PMC
     
  • [458]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982134
    A. Castellani, S. Schmitt, and B. Hammer, “Task-Sensitive Concept Drift Detector with Constraint Embedding”, 2021 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, 2021, pp.01-08.
    PUB | DOI
     
  • [457]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982136
    J. Jakob, M. Hasenjäger, and B. Hammer, “On the suitability of incremental learning for regression tasks in exoskeleton control”, 2021 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, 2021, pp.1-8.
    PUB | DOI
     
  • [456]
    2021 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982165
    A. Liuliakov and B. Hammer, “AutoML Technologies for the Identification of Sparse Models”, Intelligent Data Engineering and Automated Learning – IDEAL 2021. 22nd International Conference, IDEAL 2021, Manchester, UK, November 25–27, 2021, Proceedings, H. Yin, et al., eds., Lecture Notes in Computer Science, vol. 13113, Cham: Springer , 2021, pp.65-75.
    PUB | DOI
     
  • [455]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969237
    A. Castellani, S. Schmitt, and B. Hammer, “Estimating the Electrical Power Output of Industrial Devices with End-to-End Time-Series Classification in the Presence of Label Noise”, Machine Learning and Knowledge Discovery in Databases. Research Track. European Conference, ECML PKDD 2021, Bilbao, Spain, September 13–17, 2021, Proceedings, Part I, N. Oliver, et al., eds., Lecture Notes in Computer Science, vol. 12975, Cham: Springer International Publishing, 2021, pp.469-484.
    PUB | DOI | Download (ext.)
     
  • [454]
    2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2957340
    A. Artelt and B. Hammer, “Efficient computation of counterfactual explanations and counterfactual metrics of prototype-based classifiers”, Neurocomputing, vol. 470, 2021, pp. 304-317.
    PUB | DOI | Download (ext.) | WoS
     
  • [453]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2957373
    A. Artelt, et al., “Contrastive Explanations for Explaining Model Adaptations”, Advances in Computational Intelligence. 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Virtual Event, June 16–18, 2021, Proceedings, Part I, I. Rojas, G. Joya, and A. Catala, eds., Lecture Notes in Computer Science, Cham: Springer , 2021, pp.101-112.
    PUB | DOI
     
  • [452]
    2021 | Preprint | PUB-ID: 2959899
    A. Artelt and B. Hammer, “Convex optimization for actionable & plausible counterfactual explanations”, arXiv: 2105.07630v1, 2021.
    PUB | Download (ext.) | arXiv
     
  • [451]
    2021 | Konferenzbeitrag | PUB-ID: 2959428
    F. Hinder, et al., “Fast Non-Parametric Conditional Density Estimation using Moment Trees”, IEEE Computational Intelligence Magazine, 2021.
    PUB
     
  • [450]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2960754
    F. Hinder, et al., “A Shape-Based Method for Concept Drift Detection and Signal Denoising”, 2021 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings, Piscataway, NJ: IEEE, 2021, pp.01-08.
    PUB | DOI
     
  • [449]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2960755
    F. Hinder, et al., “Fast Non-Parametric Conditional Density Estimation using Moment Trees”, 2021 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings, Piscataway, NJ: IEEE, 2021, pp.1-7.
    PUB | DOI
     
  • [448]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2960685
    V. Vaquet, et al., “Investigating Intensity and Transversal Drift in Hyperspectral Imaging Data”, ESANN 2021 proceedings, M. Verleysen, ed., Louvain-la-Neuve (Belgium): Ciaco - i6doc.com, 2021, pp.47-52.
    PUB | DOI
     
  • [447]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2960687
    V. Vaquet, et al., “Online Learning on Non-Stationary Data Streams for Image Recognition using Deep Embeddings”, IEEE Symposium Series on Computational Intelligence, 2021, pp. 1-7.
    PUB | DOI
     
  • [446]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2962747
    A. Artelt, et al., “Evaluating Robustness of Counterfactual Explanations”, 2021 IEEE Symposium Series on Computational Intelligence (SSCI), Piscataway, NJ: IEEE, 2021, pp.01-09.
    PUB | DOI
     
  • [445]
    2021 | Report | Veröffentlicht | PUB-ID: 2954239
    J. Szczuka, et al., Können Kinder aufgeklärte Nutzer* innen von Sprachassistenten sein? Rechtliche, psychologische, ethische und informatische Perspektiven, Essen: Universität Duisburg-Essen, Universitätsbibliothek, 2021.
    PUB | DOI
     
  • [444]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2957588
    A. Artelt and B. Hammer, “Efficient computation of contrastive explanations”, 2021 International Joint Conference on Neural Networks (IJCNN), New York: Institute of Electrical and Electronics Engineers (IEEE), 2021, pp.1-9.
    PUB | DOI | Download (ext.)
     
  • [443]
    2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2949334 OA
    K. Rohlfing, et al., “Explanation as a social practice: Toward a conceptual framework for the social design of AI systems”, IEEE Transactions on Cognitive and Developmental Systems, vol. 13, 2021, pp. 717--728.
    PUB | PDF | DOI | WoS
     
  • [442]
    2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2954542
    B. Paaßen, A. Schulz, and B. Hammer, “Reservoir Stack Machines”, Neurocomputing, vol. 470, 2021, pp. 352-364.
    PUB | DOI | Download (ext.) | WoS | arXiv
     
  • [441]
    2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2959418
    J.P. Göpfert, et al., “Intuitiveness in Active Teaching”, IEEE Transactions on Human-Machine Systems, 2021, pp. 1-10.
    PUB | DOI | WoS
     
  • [440]
    2021 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2956229
    B. Paassen, et al., “Reservoir Memory Machines as Neural Computers”, IEEE Transactions on Neural Networks and Learning Systems, 2021, pp. 1-11.
    PUB | DOI | Download (ext.) | WoS | PubMed | Europe PMC | arXiv
     
  • [439]
    2021 | Zeitschriftenaufsatz | Angenommen | PUB-ID: 2955245
    D. Stallmann, et al., “Towards an automatic analysis of CHO-K1 suspension growth in microfluidic single-cell cultivation”, Bioinformatics , Accepted.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [438]
    2021 | Konferenzbeitrag | PUB-ID: 2958664
    L. Hermes, B. Hammer, and M. Schilling, “Application of Graph Convolutions in a Lightweight Model for Skeletal Human Motion Forecasting”, ESANN 2021 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. , 2021, pp.111-116.
    PUB | arXiv
     
  • [437]
    2021 | Konferenzbeitrag | Angenommen | PUB-ID: 2956774
    F. Hinder and B. Hammer, “Concept Drift Segmentation via Kolmogorov Trees”, Proceedings of the ESANN, 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, M. Verleysen, ed., Accepted.
    PUB
     
  • [436]
    2021 | Konferenzbeitrag | Angenommen | PUB-ID: 2955948
    J. Brinkrolf and B. Hammer, “Federated Learning Vector Quantization”, Proceedings of the ESANN, 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, M. Verleysen, ed., Accepted.
    PUB
     
  • [435]
    2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2952937 OA
    J. Kummert, et al., “Efficient Reject Options for Particle Filter Object Tracking in Medical Applications”, Sensors, vol. 21, 2021, : 2114.
    PUB | PDF | DOI | WoS | PubMed | Europe PMC
     
  • [434]
    2021 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2955115
    M. Straat, et al., “Supervised learning in the presence of concept drift: a modelling framework”, Neural Computing and Applications, 2021.
    PUB | DOI | WoS
     
  • [433]
    2020 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2958328
    V. Vaquet and B. Hammer, “Balanced SAM-kNN: Online Learning with Heterogeneous Drift and Imbalanced Data”, Artificial Neural Networks and Machine Learning – ICANN 2020. 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part II, I. Farkaš, P. Masulli, and S. Wermter, eds., Lecture Notes in Computer Science, vol. 12397, Cham: Springer, 2020, pp.850-862.
    PUB | DOI
     
  • [432]
    2020 | Konferenzbeitrag | PUB-ID: 2946488
    F. Hinder, A. Artelt, and B. Hammer, “Towards non-parametric drift detection via Dynamic Adapting Window Independence Drift Detection (DAWIDD)”, Proceedings of the 37th International Conference on Machine Learning, 2020.
    PUB | Download (ext.)
     
  • [431]
    2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2946685
    A. Artelt and B. Hammer, “Efficient computation of counterfactual explanations of LVQ models”, ESANN 2020 - proceedings, M. Verleysen, ed., Louvain-la-Neuve: Ciaco , 2020, pp.19-24.
    PUB | Download (ext.)
     
  • [430]
    2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2946761
    A. Artelt and B. Hammer, “Convex Density Constraints for Computing Plausible Counterfactual Explanations”, Artificial Neural Networks and Machine Learning - ICANN 2020 - 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15-18, 2020, Proceedings, Part {I}, I. Farkas, P. Masulli, and S. Wermter, eds., Lecture Notes in Computer Science, vol. 12396, Cham: Springer, 2020, pp.353-365.
    PUB | DOI | Download (ext.)
     
  • [429]
    2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2957814
    N. Krämer, et al., “Improving and Evaluating Conversational User Interfaces for Children”, IUI '20: Proceedings of the 25th International Conference on Intelligent User Interfaces, New York: Association for Computing Machinery, 2020.
    PUB
     
  • [428]
    2020 | Konferenzbeitrag | PUB-ID: 2943260
    A. Schulz, F. Hinder, and B. Hammer, “DeepView: Visualizing Classification Boundaries of Deep Neural Networks as Scatter Plots Using Discriminative Dimensionality Reduction”, Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, {IJCAI-20}, 2020.
    PUB | DOI | Download (ext.) | arXiv
     
  • [427]
    2020 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982081
    M. Biehl, et al., “Prototype-Based Classifiers in the Presence of Concept Drift: A Modelling Framework”, Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization. Proceedings of the 13th International Workshop, WSOM+ 2019, Barcelona, Spain, June 26-28, 2019, A. Vellido, et al., eds., Advances in Intelligent Systems and Computing, Cham: Springer International Publishing, 2020, pp.210-221.
    PUB | DOI
     
  • [426]
    2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2940666
    J. Brinkrolf and B. Hammer, “Sparse Metric Learning in Prototype-based Classification”, Proceedings of the ESANN, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, M. Verleysen, ed., 2020, pp.375-380.
    PUB
     
  • [425]
    2020 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2939517
    L. Pfannschmidt, et al., “Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information”, Neurocomputing, 2020.
    PUB | DOI | Download (ext.) | WoS | arXiv
     
  • [424]
    2020 | Report | Veröffentlicht | PUB-ID: 2946614 OA
    B. Hammer, et al., Sustainability and Trust for Artificial Intelligence Technologies, 2020.
    PUB | PDF | DOI
     
  • [423]
    2020 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2942892
    L.S. Iliadis, V. Kurkova, and B. Hammer, “Brain-inspired computing and machine learning”, NEURAL COMPUTING & APPLICATIONS, 2020.
    PUB | DOI | WoS
     
  • [422]
    2020 | Preprint | Entwurf | PUB-ID: 2942271 OA
    L. Pfannschmidt and B. Hammer, “Sequential Feature Classification in the Context of Redundancies”, Draft.
    PUB | PDF | arXiv
     
  • [421]
    2019 | Preprint | PUB-ID: 2959898
    A. Artelt and B. Hammer, “On the computation of counterfactual explanations - A survey”, arXiv: 1911.07749v1, 2019.
    PUB | Download (ext.) | arXiv
     
  • [420]
    2019 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982085
    J.P. Göpfert, H. Wersing, and B. Hammer, “Recovering Localized Adversarial Attacks”, Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation. 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part I, I.V. Tetko, et al., eds., Lecture Notes in Computer Science, Cham: Springer International Publishing, 2019, pp.302-311.
    PUB | DOI
     
  • [419]
    2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982084
    V. Losing, et al., “Personalized Online Learning of Whole-Body Motion Classes using Multiple Inertial Measurement Units”, 2019 International Conference on Robotics and Automation (ICRA), IEEE, 2019, pp.9530-9536.
    PUB | DOI
     
  • [418]
    2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982082
    B. Hosseini and B. Hammer, “Large-Margin Multiple Kernel Learning for Discriminative Features Selection and Representation Learning”, 2019 International Joint Conference on Neural Networks (IJCNN), IEEE, 2019, pp.1-8.
    PUB | DOI
     
  • [417]
    2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982083
    P. Li, O. Niggemann, and B. Hammer, “On the Identification of Decision Boundaries for Anomaly Detection in CPPS”, 2019 IEEE International Conference on Industrial Technology (ICIT), IEEE, 2019, pp.1311-1316.
    PUB | DOI
     
  • [416]
    2019 | Monographie | PUB-ID: 2935200 OA
    B. Paaßen, A. Artelt, and B. Hammer, Lecture Notes on Applied Optimization, Faculty of Technology, Bielefeld University: 2019.
    PUB | Dateien verfügbar
     
  • [415]
    2019 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2934458 OA
    C. Prahm, et al., “Counteracting Electrode Shifts in Upper-Limb Prosthesis Control via Transfer Learning”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, 2019, pp. 956-962.
    PUB | PDF | DOI | WoS | PubMed | Europe PMC
     
  • [414]
    2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2933893
    L. Pfannschmidt, et al., “Feature Relevance Bounds for Ordinal Regression”, Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019), M. Verleysen, ed., Louvain-la-Neuve: i6doc, 2019.
    PUB | Download (ext.) | arXiv
     
  • [413]
    2019 | Konferenzbeitrag | Angenommen | PUB-ID: 2937842 OA
    B. Hosseini and B. Hammer, “Deep-Aligned Convolutional Neural Network for Skeleton-based Action Recognition and Segmentation”, Presented at the 2019 IEEE International Conference on Data Mining (ICDM), Beijing, Accepted.
    PUB | Datei | arXiv
     
  • [412]
    2019 | Konferenzbeitrag | Angenommen | PUB-ID: 2937841 OA
    B. Hosseini and B. Hammer, “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, Accepted.
    PUB | Datei | arXiv
     
  • [411]
    2019 | Report | Veröffentlicht | PUB-ID: 2937888
    N. Krämer, et al., KI-basierte Sprachassistenten im Alltag: Forschungsbedarf aus informatischer, psychologischer, ethischer und rechtlicher Sicht, Universität Duisburg-Essen, Universitätsbibliothek, 2019.
    PUB | DOI | Download (ext.)
     
  • [410]
    2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2937839 OA
    B. Hosseini and B. Hammer, “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.
    PUB | Datei | arXiv
     
  • [409]
    2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2935456 OA
    L. Pfannschmidt, et al., “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, 2019.
    PUB | PDF | DOI | arXiv
     
  • [408]
    2019 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2933715 OA
    J. Brinkrolf, C. Göpfert, and B. Hammer, “Differential privacy for learning vector quantization”, Neurocomputing, vol. 342, 2019, pp. 125-136.
    PUB | PDF | DOI | WoS
     
  • [407]
    2019 | Konferenzbeitrag | PUB-ID: 2930303
    B. Hosseini and B. Hammer, “Multiple-Kernel Dictionary Learning for Reconstruction and Clustering of Unseen Multivariate Time-series”, Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019), M. Verleysen, ed., 2019.
    PUB | arXiv
     
  • [406]
    2019 | Konferenzbeitrag | PUB-ID: 2934192
    B. Hosseini and B. Hammer, “Large-Margin Multiple Kernel Learning for Discriminative Features Selection and Representation Learning”, Presented at the The 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, 2019.
    PUB | arXiv
     
  • [405]
    2019 | Preprint | Veröffentlicht | PUB-ID: 2934181
    J.P. Göpfert, H. Wersing, and B. Hammer, “Adversarial attacks hidden in plain sight”, 2019.
    PUB | DOI | arXiv
     
  • [404]
    2019 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2932914
    J. Brinkrolf and B. Hammer, “Time integration and reject options for probabilistic output of pairwise LVQ”, Neural Computing and Applications, 2019.
    PUB | DOI | WoS
     
  • [403]
    2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2931283 OA
    J. Queißer, et al., “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.
    PUB | PDF
     
  • [402]
    2018 | Datenpublikation | PUB-ID: 2930611 OA
    F. Hülsmann, et al., 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.
    PUB | Dateien verfügbar | DOI
     
  • [401]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2930862
    F. Hülsmann, et al., “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, vol. 76, 2018, pp. 47-59.
    PUB | DOI | Download (ext.) | WoS
     
  • [400]
    2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982092
    J.F. Queisser, et al., “Skill Memories for Parameterized Dynamic Action Primitives on the Pneumatically Driven Humanoid Robot Child Affetto”, 2018 Joint IEEE 8th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), IEEE, 2018, pp.39-45.
    PUB | DOI
     
  • [399]
    2018 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982090
    B. Hosseini and B. Hammer, “Non-negative Local Sparse Coding for Subspace Clustering”, Advances in Intelligent Data Analysis XVII. 17th International Symposium, IDA 2018, ’s-Hertogenbosch, The Netherlands, October 24–26, 2018, Proceedings, W. Duivesteijn, A. Siebes, and A. Ukkonen, eds., Lecture Notes in Computer Science, Cham: Springer International Publishing, 2018, pp.137-150.
    PUB | DOI
     
  • [398]
    2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982089
    F. Specht, et al., “Generation of Adversarial Examples to Prevent Misclassification of Deep Neural Network based Condition Monitoring Systems for Cyber-Physical Production Systems”, 2018 IEEE 16th International Conference on Industrial Informatics (INDIN), IEEE, 2018, pp.760-765.
    PUB | DOI
     
  • [397]
    2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982088
    V. Losing, H. Wersing, and B. Hammer, “Enhancing Very Fast Decision Trees with Local Split-Time Predictions”, 2018 IEEE International Conference on Data Mining (ICDM), IEEE, 2018, pp.287-296.
    PUB | DOI
     
  • [396]
    2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982087
    B. Hosseini and B. Hammer, “Confident Kernel Sparse Coding and Dictionary Learning”, 2018 IEEE International Conference on Data Mining (ICDM), IEEE, 2018, pp.1031-1036.
    PUB | DOI
     
  • [395]
    2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982086
    P. Li, O. Niggemann, and B. Hammer, “A Geometric Approach to Clustering Based Anomaly Detection for Industrial Applications”, IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, IEEE, 2018, pp.5345-5352.
    PUB | DOI
     
  • [394]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2932412
    M. Straat, et al., “Statistical Mechanics of On-Line Learning Under Concept Drift”, ENTROPY, vol. 20, 2018, : 775.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [393]
    2018 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2917896
    M. Lux, et al., “flowLearn: Fast and precise identification and quality checking of cell populations in flow cytometry”, Bioinformatics, vol. 34, 2018, pp. 2245-2253.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [392]
    2018 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2933557
    S. Meyer, et al., “Inferring Temporal Structure from Predictability in Bumblebee Learning Flight”, Intelligent Data Engineering and Automated Learning – IDEAL 2018, H. Yin, et al., eds., Lecture Notes in Computer Science, vol. 11314, Cham: Springer International Publishing, 2018, pp.508-519.
    PUB | DOI
     
  • [391]
    2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2918254
    J. Brinkrolf, K. Berger, and B. Hammer, “Differential private relevance learning”, Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018), M. Verleysen, ed., 2018, pp.555-560.
    PUB | Download (ext.)
     
  • [390]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2911900
    B. Paaßen, C. Göpfert, and B. Hammer, “Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces”, Neural Processing Letters, vol. 48, 2018, pp. 669-689.
    PUB | DOI | Download (ext.) | WoS | arXiv
     
  • [389]
    2018 | Preprint | Veröffentlicht | PUB-ID: 2921209 OA
    B. Hosseini and B. Hammer, “Non-Negative Local Sparse Coding for Subspace Clustering”, Advances in Intelligent Data Analysis XVII. IDA 2018, 2018.
    PUB | Datei | Download (ext.) | arXiv
     
  • [388]
    2018 | Konferenzbeitrag | Im Druck | PUB-ID: 2932116 OA
    B. Hosseini and B. Hammer, “Confident Kernel Sparse Coding and Dictionary Learning”, 2018 IEEE International Conference on Data Mining (ICDM), In Press.
    PUB | Datei | arXiv
     
  • [387]
    2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2919598
    B. Hosseini and B. Hammer, “Feasibility Based Large Margin Nearest Neighbor Metric Learning”, ESANN 2018. Proceedings of 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2018, pp.219-224.
    PUB | arXiv
     
  • [386]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2914505
    B. Paaßen, et al., “Expectation maximization transfer learning and its application for bionic hand prostheses”, Neurocomputing, vol. 298, 2018, pp. 122-133.
    PUB | DOI | Download (ext.) | WoS | arXiv
     
  • [385]
    2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2921316 OA
    J.P. Göpfert, B. Hammer, and H. Wersing, “Mitigating Concept Drift via Rejection”, Artificial Neural Networks and Machine Learning – ICANN 2018. Proceedings, Part I, V. Kurkova, et al., eds., Lecture Notes in Computer Science, vol. 11139, Cham: Springer, 2018.
    PUB | PDF | DOI
     
  • [384]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2917201
    V. Losing, B. Hammer, and H. Wersing, “Tackling heterogeneous concept drift with the Self-Adjusting Memory (SAM)”, KNOWLEDGE AND INFORMATION SYSTEMS, vol. 54, 2018, pp. 171-201.
    PUB | DOI | WoS
     
  • [383]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2915273 OA
    C. Göpfert, et al., “Interpretation of Linear Classifiers by Means of Feature Relevance Bounds”, Neurocomputing, vol. 298, 2018, pp. 69-79.
    PUB | PDF | DOI | WoS
     
  • [382]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2913389
    B. Paaßen, et al., “The Continuous Hint Factory - Providing Hints in Vast and Sparsely Populated Edit Distance Spaces”, Journal of Educational Data Mining, vol. 10, 2018, pp. 1-35.
    PUB | Download (ext.) | arXiv
     
  • [381]
    2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2919844
    B. Paaßen, et al., “Tree Edit Distance Learning via Adaptive Symbol Embeddings”, Proceedings of the 35th International Conference on Machine Learning (ICML 2018), J. Dy and A. Krause, eds., Proceedings of Machine Learning Research, vol. 80, 2018, pp.3973-3982.
    PUB | Download (ext.) | arXiv
     
  • [380]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2914730 OA
    V. Losing, B. Hammer, and H. Wersing, “Incremental on-line learning: A review and comparison of state of the art algorithms”, Neurocomputing, vol. 275, 2018, pp. 1261-1274.
    PUB | PDF | DOI | WoS
     
  • [379]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2918244
    J. Brinkrolf and B. Hammer, “Interpretable Machine Learning with Reject Option”, at - Automatisierungstechnik, vol. 66, 2018, pp. 283-290.
    PUB | DOI | WoS
     
  • [378]
    2018 | Konferenzbeitrag | PUB-ID: 2916318
    K. Berger, et al., “Linear Supervised Transfer Learning for the Large Margin Nearest Neighbor Classifier”, Presented at the SSCI CIDM 2017, 2018.
    PUB | DOI
     
  • [377]
    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982095
    B. Frenay and B. Hammer, “Label-noise-tolerant classification for streaming data”, 2017 International Joint Conference on Neural Networks (IJCNN), IEEE, 2017, pp.1748-1755.
    PUB | DOI
     
  • [376]
    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982091
    V. Losing, B. Hammer, and H. Wersing, “Personalized maneuver prediction at intersections”, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), IEEE, 2017, pp.1-6.
    PUB | DOI
     
  • [375]
    2017 | Kurzbeitrag Konferenz / Poster | PUB-ID: 2919987 OA
    B. Hosseini and B. Hammer, “Non-negative Kernel Sparse Coding Frameworks for Efficient Analysis of Motion Data”, Presented at the BMVA Symposium on Human Activity Recognition and Monitoring, London, 2017.
    PUB | PDF
     
  • [374]
    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909369 OA
    B. Paaßen, et al., “An EM transfer learning algorithm with applications in bionic hand prostheses”, Proceedings of the 25th European Symposium on Artificial Neural Networks (ESANN 2017), M. Verleysen, ed., Bruges: i6doc.com, 2017, pp.129-134.
    PUB | PDF
     
  • [373]
    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2914945
    J. Brinkrolf and B. Hammer, “Probabilistic extension and reject options for pairwise LVQ”, 2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM), Piscataway, NJ: IEEE, 2017.
    PUB | DOI
     
  • [372]
    2017 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2909372 OA
    A. Schulz, J. Brinkrolf, and B. Hammer, “Efficient Kernelization of Discriminative Dimensionality Reduction”, Neurocomputing, vol. 268, 2017, pp. 34-41.
    PUB | PDF | DOI | WoS
     
  • [371]
    2017 | Konferenzbeitrag | PUB-ID: 2909371
    M. Biehl, B. Hammer, and T. Villmann, “Prototype based models for the supervised learning of classificaton schemes”, Proc. of the IAU Symposium 325 on Astroinformatics, Sorrento/Italy, October 2016, 2017, pp.in press.
    PUB
     
  • [370]
    2017 | Konferenzbeitrag | PUB-ID: 2914950
    J. Brinkrolf, K. Berger, and B. Hammer, “Differential Privacy for Learning Vector Quantization”, New Challenges in Neural Computation, 2017.
    PUB
     
  • [369]
    2017 | Konferenzbeitrag | PUB-ID: 2914734 OA
    V. Losing, B. Hammer, and H. Wersing, “Self-Adjusting Memory: How to Deal with Diverse Drift Types”, Presented at the International Joint Conference on Artificial Intelligence (IJCAI) 2017, Melbourne, International Joint Conferences on Artificial Intelligence, 2017.
    PUB | PDF | DOI
     
  • [368]
    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2908201 OA
    C. Göpfert, L. Pfannschmidt, and B. Hammer, “Feature Relevance Bounds for Linear Classification”, Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, M. Verleysen, ed., Louvain-la-Neuve: Ciaco - i6doc.com, 2017, pp.187--192.
    PUB | Dateien verfügbar | Download (ext.)
     
  • [367]
    2017 | Konferenzbeitrag | PUB-ID: 2914732 OA
    V. Losing, B. Hammer, and H. Wersing, “Personalized Maneuver Prediction at Intersections”, Presented at the IEEE Intelligent Transportation Systems Conference 2017, Yokohama, 2017.
    PUB | PDF
     
  • [366]
    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2913752 OA
    J.P. Göpfert, et al., “Effects of Variability in Synthetic Training Data on Convolutional Neural Networks for 3D Head Reconstruction”, 2017 SSCI Proceedings. 2017 IEEE Symposium Series on Computational Intelligence (SSCI), Piscataway, NJ: IEEE, 2017.
    PUB | PDF | DOI
     
  • [365]
    2017 | Kurzbeitrag Konferenz / Poster | PUB-ID: 2919990 OA
    B. Hosseini and B. Hammer, “Task-Driven Sparse Coding for Classification of Motion Data”, Presented at the Ninth Mittweida Workshop on Computational Intelligence (MiWoCI 2017), Mittweida, 2017.
    PUB | PDF
     
  • [364]
    2017 | Konferenzbeitrag | PUB-ID: 2909370
    B. Frenay and B. Hammer, “Label-Noise-Tolerant Classification for Streaming Data”, IEEE International Joint Conference on Neural Neworks, 2017.
    PUB
     
  • [363]
    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2914141 OA
    W. Aswolinskiy and B. Hammer, “Unsupervised Transfer Learning for Time Series via Self-Predictive Modelling - First Results”, Proceedings of the Workshop on New Challenges in Neural Computation (NC2), Machine Learning Reports, vol. 03/2017, Bielefeld: Universität Bielefeld, CITEC, 2017.
    PUB | PDF
     
  • [362]
    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909037 OA
    C. Prahm, et al., “Echo State Networks as Novel Approach for Low-Cost Myoelectric Control”, Proceedings of the 16th Conference on Artificial Intelligence in Medicine (AIME 2017), A. ten Telje, et al., eds., Lecture Notes in Computer Science, vol. 10259, Springer, 2017, pp.338--342.
    PUB | Dateien verfügbar | DOI
     
  • [361]
    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2915274 OA
    C. Göpfert, J.P. Göpfert, and B. Hammer, “Analyzing Feature Relevance for Linear Reject Option SVM using Relevance Intervals”, Proceedings of the 2017 NIPS workshop on Transparent and Interpretable Machine Learning in Safety Critical Environments, 2017.
    PUB | PDF
     
  • [360]
    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2904469
    B. Hosseini, et al., “Non-Negative Kernel Sparse Coding for the Analysis of Motion Data”, Artificial Neural Networks and Machine Learning – ICANN 2016, A. E.P. Villa, P. Masulli, and A. Javier Pons Rivero, eds., Lecture Notes in Computer Science, vol. 9887, Cham: Springer, 2016, pp.506-514.
    PUB | PDF | DOI | Download (ext.) | arXiv
     
  • [359]
    2016 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2982097
    M. Biehl, B. Hammer, and T. Villmann, “Prototype-based Models for the Supervised Learning of Classification Schemes”, Proceedings of the International Astronomical Union, vol. 12, 2016, pp. 129-138.
    PUB | DOI
     
  • [358]
    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982096
    L. Fischer, B. Hammer, and H. Wersing, “Online metric learning for an adaptation to confidence drift”, 2016 International Joint Conference on Neural Networks (IJCNN), IEEE, 2016, pp.748-755.
    PUB | DOI
     
  • [357]
    2016 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2907633 OA
    M. Lux, et al., “acdc – Automated Contamination Detection and Confidence estimation for single-cell genome data”, BMC Bioinformatics, vol. 17, 2016, : 543.
    PUB | PDF | DOI | WoS | PubMed | Europe PMC
     
  • [356]
    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909367
    J. Kummert, et al., “Local Reject Option for Deterministic Multi-class SVM”, Artificial Neural Networks and Machine Learning - ICANN 2016 - 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II, A. E.P. Villa, P. Masulli, and A.J. Pons Rivero, eds., Lecture Notes in Computer Science, vol. 9887, Cham: Springer Nature, 2016, pp.251--258.
    PUB | DOI
     
  • [355]
    2016 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2783224 OA
    B. Paaßen, B. Mokbel, and B. Hammer, “Adaptive structure metrics for automated feedback provision in intelligent tutoring systems”, Neurocomputing, vol. 192, 2016, pp. 3-13.
    PUB | PDF | DOI | WoS
     
  • [354]
    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2904509
    B. Paaßen, J. Jensen, and B. Hammer, “Execution Traces as a Powerful Data Representation for Intelligent Tutoring Systems for Programming”, Proceedings of the 9th International Conference on Educational Data Mining, T. Barnes, M. Chi, and M. Feng, eds., Raleigh, North Carolina, USA: International Educational Datamining Society, 2016, pp.183-190.
    PUB | Download (ext.)
     
  • [353]
    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900676 OA
    B. Paaßen, C. Göpfert, and B. Hammer, “Gaussian process prediction for time series of structured data”, Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, M. Verleysen, ed., Louvain-la-Neuve: Ciaco - i6doc.com, 2016, pp.41--46.
    PUB | PDF
     
  • [352]
    2016 | Konferenzbeitrag | E-Veröff. vor dem Druck | PUB-ID: 2904909 OA
    A. Schulz and B. Hammer, “Discriminative Dimensionality Reduction in Kernel Space”, ESANN2016 Proceedings. 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges, Belgium,27-29 April 2016, i6doc.com, 2016.
    PUB | PDF
     
  • [351]
    2016 | Konferenzbeitrag | PUB-ID: 2909365
    J. Brinkrolf, et al., “Virtual optimisation for improved production planning”, New Challenges in Neural Computation, 2016.
    PUB
     
  • [350]
    2016 | Konferenzbeitrag | PUB-ID: 2907624 OA
    V. Losing, B. Hammer, and H. Wersing, “Choosing the Best Algorithm for an Incremental On-line Learning Task”, Presented at the European Symposium on Artificial Neural Networks, Brügge, 2016.
    PUB | PDF
     
  • [349]
    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2905729 OA
    C. Göpfert, B. Paaßen, and B. Hammer, “Convergence of Multi-pass Large Margin Nearest Neighbor Metric Learning”, Artificial Neural Networks and Machine Learning – ICANN 2016: 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II, A. E.P. Villa, P. Masulli, and A.J. Pons Rivero, eds., Lecture Notes in Computer Science, vol. 9887, Cham: Springer Nature, 2016, pp.510-517.
    PUB | PDF | DOI
     
  • [348]
    2016 | Konferenzbeitrag | PUB-ID: 2908455 OA
    V. Losing, B. Hammer, and H. Wersing, “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.
    PUB | PDF
     
  • [347]
    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2905855
    B. Paaßen, A. Schulz, and B. Hammer, “Linear Supervised Transfer Learning for Generalized Matrix LVQ”, Proceedings of the Workshop New Challenges in Neural Computation 2016, B. Hammer, T. Martinetz, and T. Villmann, eds., Machine Learning Reports, 2016, pp.11-18.
    PUB | Download (ext.)
     
  • [346]
    2016 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2903457
    F.-M. Schleif, et al., “Odor recognition in robotics applications by discriminative time-series modeling”, PATTERN ANALYSIS AND APPLICATIONS, vol. 19, 2016, pp. 207-220.
    PUB | DOI | WoS
     
  • [345]
    2016 | Konferenzbeitrag | PUB-ID: 2909368
    er Geppert and B. Hammer, “Incremental learning algorithms and applications”, ESANN, 2016.
    PUB
     
  • [344]
    2016 | Konferenzbeitrag | PUB-ID: 2905195
    L. Fischer, B. Hammer, and H. Wersing, “Online Metric Learning for an Adaptation to Confidence Drift”, Proceedings of International Joint Conference on Neural Networks (IJCNN), Vancouver: IEEE, 2016, pp.748-755.
    PUB
     
  • [343]
    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2904178 OA
    C. Prahm, et al., “Transfer Learning for Rapid Re-calibration of a Myoelectric Prosthesis after Electrode Shift”, Converging Clinical and Engineering Research on Neurorehabilitation II: Proceedings of the 3rd International Conference on NeuroRehabilitation (ICNR2016), J. Ibáñez, et al., eds., Springer, 2016, pp.153--157.
    PUB | PDF | DOI | Download (ext.)
     
  • [342]
    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2907622 OA
    V. Losing, B. Hammer, and H. Wersing, “KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift”, 2016 IEEE 16th International Conference on Data Mining (ICDM), Piscataway, NJ: IEEE, 2016, pp.291-300.
    PUB | PDF | DOI
     
  • [341]
    2016 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2910957
    M. Biehl, B. Hammer, and T. Villmann, “Prototype-based models in machine learning”, Wiley Interdisciplinary Reviews: Cognitive Science, vol. 7, 2016, pp. 92-111.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [340]
    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909366
    T. Villmann, et al., “Self-Adjusting Reject Options in Prototype Based Classification”, Advances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 11th International Workshop WSOM 2016, Houston, Texas, USA, January 6-8, 2016, E. Merényi, M.J. Mendenhall, and P. O'Driscoll, eds., Advances in Intelligent Systems and Computing, vol. 428, Cham: Springer International Publishing, 2016, pp.269-279.
    PUB | DOI
     
  • [339]
    2016 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2905193
    L. Fischer, B. Hammer, and H. Wersing, “Optimal local rejection for classifiers”, Neurocomputing, vol. 214, 2016, pp. 445-457.
    PUB | DOI | WoS
     
  • [338]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982098
    L. Fischer, B. Hammer, and H. Wersing, “Combining offline and online classifiers for life-long learning”, 2015 International Joint Conference on Neural Networks (IJCNN), IEEE, 2015, pp.1-8.
    PUB | DOI
     
  • [337]
    2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2752948 OA
    S. Gross, et al., “Learning Feedback in Intelligent Tutoring Systems. Report of the FIT Project, Conducted from December 2011 to March 2015”, KI - Künstliche Intelligenz, vol. 29, 2015, pp. 413-418.
    PUB | PDF | DOI | Download (ext.) | WoS
     
  • [336]
    2015 | Preprint | Veröffentlicht | PUB-ID: 2901613
    M. Lux, B. Hammer, and A. Sczyrba, “Automated Contamination Detection in Single-Cell Sequencing”, bioRxiv, 2015.
    PUB
     
  • [335]
    2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2671047 OA
    A. Gisbrecht, A. Schulz, and B. Hammer, “Parametric nonlinear dimensionality reduction using kernel t-SNE”, Neurocomputing, vol. 147, 2015, pp. 71-82.
    PUB | PDF | DOI | WoS
     
  • [334]
    2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2909226
    A. Gisbrecht and B. Hammer, “Data visualization by nonlinear dimensionality reduction”, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 5, 2015, pp. 51-73.
    PUB | DOI | WoS
     
  • [333]
    2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2759763
    F.-M. Schleif, X. Zhu, and B. Hammer, “Sparse conformal prediction for dissimilarity data”, Annals of Mathematics and Artificial Intelligence, vol. 74, 2015, pp. 95-116.
    PUB | DOI | WoS
     
  • [332]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2783165
    B. Hosseini and B. Hammer, “Efficient Metric Learning for the Analysis of Motion Data”, 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA), Piscataway, NJ: IEEE, 2015.
    PUB | DOI | Download (ext.) | arXiv
     
  • [331]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2903777 OA
    A. Schulz, et al., “Inferring Feature Relevances From Metric Learning”, 2015 IEEE Symposium Series on Computational Intelligence, Piscataway, NJ: IEEE, 2015.
    PUB | PDF | DOI
     
  • [330]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2724156 OA
    B. Paaßen, B. Mokbel, and B. Hammer, “Adaptive structure metrics for automated feedback provision in Java programming”, Proceedings of the ESANN, 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, M. Verleysen, ed., 2015, pp.307-312.
    PUB | PDF
     
  • [329]
    2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2710031 OA
    B. Mokbel, et al., “Metric learning for sequences in relational LVQ”, Neurocomputing, vol. 169, 2015, pp. 306-322.
    PUB | PDF | DOI | Download (ext.) | WoS
     
  • [328]
    2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2766822 OA
    A. Schulz, A. Gisbrecht, and B. Hammer, “Using Discriminative Dimensionality Reduction to Visualize Classifiers”, Neural Processing Letters, vol. 42, 2015, pp. 27-54.
    PUB | PDF | DOI | WoS
     
  • [327]
    2015 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2900303 OA
    A. Schulz and B. Hammer, “Visualization of Regression Models Using Discriminative Dimensionality Reduction”, Computer Analysis of Images and Patterns, Lecture Notes in Computer Science, vol. 9257, Cham: Springer Science + Business Media, 2015, pp.437-449.
    PUB | PDF | DOI
     
  • [326]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900325 OA
    P. Blöbaum, A. Schulz, and B. Hammer, “Unsupervised Dimensionality Reduction for Transfer Learning”, Proceedings. 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, M. Verleysen, ed., Louvain-la-Neuve: Ciaco, 2015, pp.507-512.
    PUB | PDF
     
  • [325]
    2015 | Zeitschriftenaufsatz | PUB-ID: 2909364
    B. Hammer and M. Toussaint, “Special Issue on Autonomous Learning”, {KI}, vol. 29, 2015, pp. 323--327.
    PUB | DOI | WoS
     
  • [324]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900319
    A. Schulz and B. Hammer, “Discriminative dimensionality reduction for regression problems using the Fisher metric”, 2015 International Joint Conference on Neural Networks (IJCNN), Institute of Electrical & Electronics Engineers (IEEE), 2015, pp.1-8.
    PUB | DOI
     
  • [323]
    2015 | Preprint | PUB-ID: 2774656
    L. Fischer, B. Hammer, and H. Wersing, “Optimum Reject Options for Prototype-based Classification”, 2015.
    PUB | arXiv
     
  • [322]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2774707
    L. Fischer, B. Hammer, and H. Wersing, “Certainty-based Prototype Insertion/Deletion for Classification with Metric Adaptation”, ESANN, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2015, pp.7-12.
    PUB
     
  • [321]
    2015 | Konferenzbeitrag | PUB-ID: 2774721
    L. Fischer, B. Hammer, and H. Wersing, “Combining Offline and Online Classifiers for Life-long Learning”, IJCNN, International Joint Conference on Neural Networks, 2015, pp.2808-2815.
    PUB
     
  • [320]
    2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2772407
    D. Nebel, et al., “Median variants of learning vector quantization for learning of dissimilarity data”, Neurocomputing, vol. 169, 2015, pp. 295-305.
    PUB | DOI | WoS
     
  • [319]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2762087
    B. Paaßen, B. Mokbel, and B. Hammer, “A Toolbox for Adaptive Sequence Dissimilarity Measures for Intelligent Tutoring Systems”, Proceedings of the 8th International Conference on Educational Data Mining, O.C. Santos, et al., eds., International Educational Datamining Society, 2015, pp.632-632.
    PUB | Download (ext.)
     
  • [318]
    2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2752955 OA
    O. Walter, et al., “Autonomous Learning of Representations”, KI - Künstliche Intelligenz, vol. 29, 2015, pp. 339–351.
    PUB | PDF | DOI | Download (ext.) | WoS
     
  • [317]
    2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2695196
    D. Hofmann, A. Gisbrecht, and B. Hammer, “Efficient approximations of robust soft learning vector quantization for non-vectorial data”, Neurocomputing, vol. 147, 2015, pp. 96-106.
    PUB | DOI | WoS
     
  • [316]
    2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2772413
    L. Fischer, B. Hammer, and H. Wersing, “Efficient rejection strategies for prototype-based classification”, Neurocomputing, vol. 169, 2015, pp. 334-342.
    PUB | DOI | WoS
     
  • [315]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2901612
    M. Lux, A. Sczyrba, and B. Hammer, “Automatic discovery of metagenomic structure”, 2015 International Joint Conference on Neural Networks (IJCNN), Institute of Electrical & Electronics Engineers (IEEE), 2015.
    PUB | DOI
     
  • [314]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900318
    A. Schulz and B. Hammer, “Metric Learning in Dimensionality Reduction”, Proceedings of the International Conference on Pattern Recognition Applications and Methods, Scitepress, 2015, pp.232-239.
    PUB | DOI
     
  • [313]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2776021 OA
    V. Losing, B. Hammer, and H. Wersing, “Interactive Online Learning for Obstacle Classification on a Mobile Robot”, Presented at the International Joint Conference on Neural Networks, Killarney, Ireland, IEEE, 2015.
    PUB | PDF | DOI
     
  • [312]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2910954
    M. Biehl, et al., “Stationarity of Matrix Relevance LVQ”, 2015 International Joint Conference on Neural Networks (IJCNN), IEEE, 2015.
    PUB | DOI
     
  • [311]
    2014 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982100
    S. Gross, et al., “How to Select an Example? A Comparison of Selection Strategies in Example-Based Learning”, Intelligent Tutoring Systems, S. Trausan-Matu, et al., eds., Lecture Notes in Computer Science, Cham: Springer International Publishing, 2014, pp.340-347.
    PUB | DOI
     
  • [310]
    2014 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982099
    M. Biehl, B. Hammer, and T. Villmann, “Distance Measures for Prototype Based Classification”, Brain-Inspired Computing. International Workshop, BrainComp 2013, Cetraro, Italy, July 8-11, 2013, Revised Selected Papers, L. Grandinetti, T. Lippert, and N. Petkov, eds., Lecture Notes in Computer Science, Cham: Springer International Publishing, 2014, pp.100-116.
    PUB | DOI
     
  • [309]
    2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900320 OA
    B. Frenay, et al., “Valid interpretation of feature relevance for linear data mappings”, 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), Piscataway, NJ: Institute of Electrical & Electronics Engineers (IEEE), 2014, pp.149-156.
    PUB | PDF | DOI
     
  • [308]
    2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2678214
    D. Hofmann, et al., “Learning interpretable kernelized prototype-based models”, Neurocomputing, vol. 141, 2014, pp. 84-96.
    PUB | DOI | Download (ext.) | WoS
     
  • [307]
    2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2672504
    X. Zhu, F.-M. Schleif, and B. Hammer, “Adaptive Conformal Semi-Supervised Vector Quantization for Dissimilarity Data”, Pattern Recognition Letters, vol. 49, 2014, pp. 138-145.
    PUB | DOI | WoS
     
  • [306]
    2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2615730
    B. Hammer, et al., “Learning vector quantization for (dis-)similarities”, NeuroComputing, vol. 131, 2014, pp. 43-51.
    PUB | DOI | WoS
     
  • [305]
    2014 | Konferenzbeitrag | PUB-ID: 2909360
    S. Gross, et al., “How to Select an Example? A Comparison of Selection Strategies in Example-Based Learning”, Intelligent Tutoring Systems, S. Trausan-Matu, et al., eds., Lecture Notes in Computer Science, vol. 8474, Springer, 2014, pp.340-347.
    PUB
     
  • [304]
    2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2694967
    Y. Jin and B. Hammer, “Computational Intelligence in Big Data”, IEEE Computational Intelligence Magazine, vol. 9, 2014, pp. 12-13.
    PUB | DOI | WoS
     
  • [303]
    2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2774643
    L. Fischer, et al., “Rejection Strategies for Learning Vector Quantization – A Comparison of Probabilistic and Deterministic Approaches”, Advances in Self-Organizing Maps and Learning Vector Quantization, T. Villmann, et al., eds., Advances in Intelligent Systems and Computing, vol. 295, Cham: Springer International Publishing, 2014, pp.109-118.
    PUB | DOI
     
  • [302]
    2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2673548
    L. Fischer, B. Hammer, and H. Wersing, “Rejection strategies for learning vector quantization”, ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, M. Verleysen, ed., Bruges, Belgium: i6doc.com, 2014, pp.41-46.
    PUB
     
  • [301]
    2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2774498
    L. Fischer, B. Hammer, and H. Wersing, “Local Rejection Strategies for Learning Vector Quantization”, Artificial Neural Networks and Machine Learning – ICANN 2014, S. Wermter, et al., eds., Lecture Notes in Computer Science, vol. 8681, Cham: Springer International Publishing, 2014, pp.563-570.
    PUB | DOI
     
  • [300]
    2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2673554 OA
    B. Mokbel, B. Paaßen, and B. Hammer, “Adaptive distance measures for sequential data”, ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, M. Verleysen, ed., Bruges, Belgium: i6doc.com, 2014, pp.265-270.
    PUB | PDF
     
  • [299]
    2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2673559
    B. Hammer, H. He, and T. Martinetz, “Learning and modeling big data”, ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, M. Verleysen, ed., Bruges, Belgium: i6doc.com, 2014, pp.343-352.
    PUB
     
  • [298]
    2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2734058
    S. Gross, et al., “Example-based feedback provision using structured solution spaces”, International Journal of Learning Technology, vol. 9, 2014, pp. 248-280.
    PUB | DOI | Download (ext.)
     
  • [297]
    2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2710067 OA
    B. Mokbel, B. Paaßen, and B. Hammer, “Efficient Adaptation of Structure Metrics in Prototype-Based Classification”, Artificial Neural Networks and Machine Learning - ICANN 2014 - 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings, S. Wermter, et al., eds., Lecture Notes in Computer Science, vol. 8681, Springer, 2014, pp.571-578.
    PUB | PDF | DOI | Download (ext.)
     
  • [296]
    2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2673545
    D. Nebel, B. Hammer, and T. Villmann, “Supervised Generative Models for Learning Dissimilarity Data”, ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, M. Verleysen, ed., Bruges, Belgium: i6doc.com, 2014, pp.35-40.
    PUB
     
  • [295]
    2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2673557
    A. Schulz, A. Gisbrecht, and B. Hammer, “Relevance learning for dimensionality reduction”, ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, M. Verleysen, ed., Bruges, Belgium: i6doc.com, 2014, pp.165-170.
    PUB
     
  • [294]
    2014 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2900324
    A. Gisbrecht, A. Schulz, and B. Hammer, “Discriminative Dimensionality Reduction for the Visualization of Classifiers”, Pattern Recognition Applications and Methods, Advances in Intelligent Systems and Computing, vol. 318, Cham: Springer Science + Business Media, 2014, pp.39-56.
    PUB | DOI
     
  • [293]
    2014 | Konferenzbeitrag | PUB-ID: 2909361
    B. Hammer, et al., “Generative versus Discriminative Prototype Based Classification”, 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, 2014, pp.123--132.
    PUB | DOI
     
  • [292]
    2013 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982105
    F.-M. Schleif, X. Zhu, and B. Hammer, “Sparse Prototype Representation by Core Sets”, Intelligent Data Engineering and Automated Learning – IDEAL 2013, H. Yin, et al., eds., Lecture Notes in Computer Science, Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, pp.302-309.
    PUB | DOI
     
  • [291]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982104
    M. Strickert, et al., “Regularization and improved interpretation of linear data mappings and adaptive distance measures”, 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2013, pp.10-17.
    PUB | DOI
     
  • [290]
    2013 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982102
    D. Hofmann, A. Gisbrecht, and B. Hammer, “Efficient Approximations of Kernel Robust Soft LVQ”, Advances in Self-Organizing Maps, P.A. Estévez, J.C. Príncipe, and P. Zegers, eds., Advances in Intelligent Systems and Computing, Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, pp.183-192.
    PUB | DOI
     
  • [289]
    2013 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982101
    D. Nebel, B. Hammer, and T. Villmann, “A Median Variant of Generalized Learning Vector Quantization”, Neural Information Processing, M. Lee, et al., eds., Lecture Notes in Computer Science, Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, pp.19-26.
    PUB | DOI
     
  • [288]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2623500
    A. Gisbrecht, et al., “Nonlinear dimensionality reduction for cluster identification in metagenomic samples”, 17th International Conference on Information Visualisation IV 2013, E. Banissi, ed., Piscataway, NJ: IEEE, 2013, pp.174-179.
    PUB | DOI
     
  • [287]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622454
    B. Hammer, A. Gisbrecht, and A. Schulz, “Applications of discriminative dimensionality reduction”, Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods, SCITEPRESS, 2013, pp.33-41.
    PUB | DOI
     
  • [286]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625185
    B. Mokbel, et al., “Domain-Independent Proximity Measures in Intelligent Tutoring Systems”, Proceedings of the 6th International Conference on Educational Data Mining (EDM), S.K. D'Mello, R.A. Calvo, and A. Olney, eds., 2013, pp.334-335.
    PUB | Download (ext.)
     
  • [285]
    2013 | Konferenzbeitrag | PUB-ID: 2909358
    M. Strickert, et al., “Regularization and Improved Interpretation of Linear Data Mappings and Adaptive Distance Measures”, IEEE SSCI CIDM 2013, IEEE Computational Intelligence Society, 2013, pp.10-17.
    PUB
     
  • [284]
    2013 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2612736
    B. Mokbel, et al., “Visualizing the quality of dimensionality reduction”, Neurocomputing, vol. 112, 2013, pp. 109-123.
    PUB | DOI | WoS
     
  • [283]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622456
    A. Schulz, A. Gisbrecht, and B. Hammer, “Using Nonlinear Dimensionality Reduction to Visualize Classifiers”, Advances in computational intelligence. Proceedings. Vol 1, I. Rojas, G. Joya, and J. Gabestany, eds., Lecture Notes in Computer Science, vol. 7902, Springer, 2013, pp.59-68.
    PUB | DOI | WoS
     
  • [282]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2670686
    S. Gross, et al., “Towards a Domain-Independent ITS Middleware Architecture”, 2013 IEEE 13th International Conference on Advanced Learning Technologies, IEEE, 2013, pp.408-409.
    PUB | DOI | WoS
     
  • [281]
    2013 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2607146
    B. Hammer, et al., “Preface: Intelligent interactive data visualization”, Data Mining and Knowledge Discovery, vol. 27, 2013, pp. 1-3.
    PUB | DOI | WoS
     
  • [280]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622467
    A. Schulz, A. Gisbrecht, and B. Hammer, “Classifier inspection based on different discriminative dimensionality reductions”, Workshop NC^2 2013, TR Machine Learning Reports, 2013, pp.77-86.
    PUB
     
  • [279]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625194
    A. Gisbrecht, et al., “Visualizing Dependencies of Spectral Features using Mutual Information”, ESANN, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2013, pp.573-578.
    PUB
     
  • [278]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625199
    D. Hofmann and B. Hammer, “Sparse approximations for kernel learning vector quantization”, ESANN, 2013.
    PUB
     
  • [277]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625202
    F.-M. Schleif, X. Zhu, and B. Hammer, “Sparse prototype representation by core sets”, IDEAL 2013, et.al Hujun Yin, ed., 2013.
    PUB
     
  • [276]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625207
    S. Gross, et al., “Towards Providing Feedback to Students in Absence of Formalized Domain Models”, AIED, 2013, pp.644-648.
    PUB
     
  • [275]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615717
    X. Zhu, F.-M. Schleif, and B. Hammer, “Secure Semi-supervised Vector Quantization for Dissimilarity Data”, IWANN (1), I. Rojas, G. Joya, and J. Cabestany, eds., Lecture Notes in Computer Science, vol. 7902, Springer, 2013, pp.347-356.
    PUB | DOI
     
  • [274]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615701
    X. Zhu, F.-M. Schleif, and B. Hammer, “Semi-Supervised Vector Quantization for proximity data”, Proceedings of ESANN 2013, 2013, pp.89-94.
    PUB
     
  • [273]
    2013 | Konferenzbeitrag | PUB-ID: 2909359
    D. Nebel, B. Hammer, and T. Villmann, “A Median Variant of Generalized Learning Vector Quantization”, ICONIP (2), 2013, pp.19-26.
    PUB
     
  • [272]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982108
    A. Gisbrecht, B. Mokbel, and B. Hammer, “Linear basis-function t-SNE for fast nonlinear dimensionality reduction”, The 2012 International Joint Conference on Neural Networks (IJCNN), IEEE, 2012, pp.1-8.
    PUB | DOI
     
  • [271]
    2012 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982106
    A. Gisbrecht, D. Hofmann, and B. Hammer, “Discriminative Dimensionality Reduction Mappings”, Advances in Intelligent Data Analysis XI, J. Hollmén, F. Klawonn, and A. Tucker, eds., Lecture Notes in Computer Science, Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp.126-138.
    PUB | DOI
     
  • [270]
    2012 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982107
    D. Hofmann and B. Hammer, “Kernel Robust Soft Learning Vector Quantization”, Artificial Neural Networks in Pattern Recognition, N. Mana, F. Schwenker, and E. Trentin, eds., Lecture Notes in Computer Science, Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp.14-23.
    PUB | DOI
     
  • [269]
    2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2625232
    A. Gisbrecht, et al., “Linear Time Relational Prototype Based Learning”, International Journal of Neural Systems, vol. 22, 2012, : 1250021.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [268]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622449
    A. Schulz, et al., “How to visualize a classifier?”, Proceedings of the Workshop - New Challenges in Neural Computation 2012, Machine Learning Reports, 2012, pp.73-83.
    PUB
     
  • [267]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625260
    A. Gisbrecht, et al., “Out-of-sample kernel extensions for nonparametric dimensionality reduction”, ESANN 2012, 2012, pp.531-536.
    PUB
     
  • [266]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625265
    A. Gisbrecht, et al., “Relevance learning for time series inspection”, ESANN 2012, M. Verleysen, ed., 2012, pp.489-494.
    PUB
     
  • [265]
    2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2625225
    B. Hammer, “Special Issue on Neural Learning Paradigms”, Künstliche Intelligenz :KI, vol. 26, 2012, pp. 329-332.
    PUB | DOI
     
  • [264]
    2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2509858
    M. Kaestner, et al., “Functional relevance learning in generalized learning vector quantization”, Neurocomputing, vol. 90, 2012, pp. 85-95.
    PUB | DOI | WoS
     
  • [263]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2671172
    D. Hofmann, A. Gisbrecht, and B. Hammer, “Discriminative probabilistic prototype based models in kernel space”, Workshop NC^2 2012, TR Machine Learning Reports, 2012.
    PUB
     
  • [262]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2536426 OA
    B. Mokbel, et al., “How to Quantitatively Compare Data Dissimilarities for Unsupervised Machine Learning?”, Artificial Neural Networks in Pattern Recognition. 5th INNS IAPR TC 3 GIRPR Workshop, ANNPR 2012. Proceedings, N. Mana, F. Schwenker, and E. Trentin, eds., Lecture Notes in Artificial Intelligence, vol. 7477, Springer Berlin Heidelberg, 2012, pp.1-13.
    PUB | PDF | DOI
     
  • [261]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625238
    D. Hofmann, A. Gisbrecht, and B. Hammer, “Efficient Approximations of Kernel Robust Soft LVQ”, WSOM, 2012.
    PUB
     
  • [260]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625271
    C. Bouveyron, B. Hammer, and T. Villmann, “Recent developments in clustering algorithms”, ESANN 2012, M. Verleysen, ed., 2012, pp.447-458.
    PUB
     
  • [259]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625276
    A. Gisbrecht, B. Mokbel, and B. Hammer, “Linear Basis-Function t-SNE for Fast Nonlinear Dimensionality Reduction”, IJCNN, 2012.
    PUB
     
  • [258]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622453
    B. Hammer, A. Gisbrecht, and A. Schulz, “How to Visualize Large Data Sets?”, Presented at the Workshop Advances in Self-Organizing Maps (WSOM), Santiago, Chile, 2012.
    PUB | DOI
     
  • [257]
    2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2625223
    B. Hammer, “Challenges in Neural Computation”, Künstliche Intelligenz : KI, vol. 26, 2012, pp. 333-340.
    PUB | DOI
     
  • [256]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625242
    S. Gross, et al., “Feedback Provision Strategies in Intelligent Tutoring Systems Based on Clustered Solution Spaces”, DeLFI, 2012, pp.27-38.
    PUB
     
  • [255]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625247
    A. Gisbrecht, D. Hofmann, and B. Hammer, “Discriminative Dimensionality Reduction Mappings”, Advances in Intelligent Data Analysis XI - 11th International Symposium, IDA 2012, Helsinki, Finland, October 25-27, 2012. Proceedings, J. Hollmén, F. Klawonn, and A. Tucker, eds., Lecture Notes in Computer Science, vol. 7619, Springer, 2012, pp.126-138.
    PUB
     
  • [254]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625254
    D. Hofmann and B. Hammer, “Kernel Robust Soft Learning Vector Quantization”, Artificial Neural Networks in Pattern Recognition - 5th INNS IAPR TC 3 GIRPR Workshop, ANNPR 2012, Trento, Italy, September 17-19, 2012. Proceedings, N. Mana, F. Schwenker, and E. Trentin, eds., Lecture Notes in Computer Science, vol. 7477, Springer, 2012, pp.14-23.
    PUB
     
  • [253]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615750
    F.-M. Schleif, et al., “Fast approximated relational and kernel clustering”, Proceedings of ICPR 2012, IEEE, 2012, pp.1229-1232.
    PUB
     
  • [252]
    2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2671281
    B. Hammer and T. Villmann, “Special issue on new challenges in neural computation 2012”, Neurocomputing, vol. 131, 2012, : 1.
    PUB | DOI | WoS
     
  • [251]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2536437 OA
    S. Gross, et al., “Cluster based feedback provision strategies in intelligent tutoring systems”, Proceedings of the 11th international conference on Intelligent Tutoring Systems, Berlin, Heidelberg: Springer-Verlag, 2012, pp.699-700.
    PUB | PDF | DOI | Download (ext.)
     
  • [250]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2536444 OA
    S. Gross, et al., “Feedback Provision Strategies in Intelligent Tutoring Systems Based on Clustered Solution Spaces”, DeLFI 2012: Die 10. e-Learning Fachtagung Informatik, J. Desel, et al., eds., GI-Edition : Proceedings, vol. 207, Hagen, Germany: Köllen, 2012, pp.27-38.
    PUB | PDF
     
  • [249]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615756
    F.-M. Schleif, X. Zhu, and B. Hammer, “Soft Competitive Learning for large data sets”, Proceedings of MCSD 2012, Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp.141-151.
    PUB | DOI
     
  • [248]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534877
    F.-M. Schleif, et al., “Learning Relevant Time Points for Time-Series Data in the Life Sciences”, ICANN (2), Lecture Notes in Computer Science, vol. 7553, Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp.531-539.
    PUB | DOI
     
  • [247]
    2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2489405
    K. Bunte, et al., “Limited Rank Matrix Learning, discriminative dimension reduction and visualization”, Neural Networks, vol. 26, 2012, pp. 159-173.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [246]
    2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2474292
    K. Bunte, M. Biehl, and B. Hammer, “A General Framework for Dimensionality-Reducing Data Visualization Mapping”, Neural Computation, vol. 24, 2012, pp. 771-804.
    PUB | DOI | WoS
     
  • [245]
    2012 | Konferenzbeitrag | PUB-ID: 2909356
    B. Mokbel, et al., “Visualizing the quality of dimensionality reduction”, ESANN 2012, M. Verleysen, ed., 2012, pp.179--184.
    PUB
     
  • [244]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534888
    F.-M. Schleif, X. Zhu, and B. Hammer, “A Conformal Classifier for Dissimilarity Data”, AIAI (2), Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp.234-243.
    PUB | DOI
     
  • [243]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534910
    X. Zhu, F.-M. Schleif, and B. Hammer, “Patch Processing for Relational Learning Vector Quantization”, ISNN (1), Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp.55-63.
    PUB | DOI
     
  • [242]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534868
    B. Hammer, et al., “White Box Classification of Dissimilarity Data”, HAIS (1), Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp.309-321.
    PUB | DOI | WoS
     
  • [241]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534905
    F.-M. Schleif, A. Gisbrecht, and B. Hammer, “Relevance learning for short high-dimensional time series in the life sciences”, IJCNN, IEEE Computational Intelligence Society and Institute of Electrical and Electronics Engineers, eds., Piscataway, NJ: IEEE, 2012, pp.1-8.
    PUB | DOI
     
  • [240]
    2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2509852
    X. Zhu, et al., “Approximation techniques for clustering dissimilarity data”, Neurocomputing, vol. 90, 2012, pp. 72-84.
    PUB | DOI | WoS
     
  • [239]
    2011 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982113
    B. Hammer, et al., “Topographic Mapping of Dissimilarity Data”, Advances in Self-Organizing Maps, J. Laaksonen and T. Honkela, eds., Lecture Notes in Computer Science, Berlin, Heidelberg: Springer Berlin Heidelberg, 2011, pp.1-15.
    PUB | DOI
     
  • [238]
    2011 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982112
    B. Hammer, F.-M. Schleif, and X. Zhu, “Relational Extensions of Learning Vector Quantization”, Neural Information Processing, B.-L. Lu, L. Zhang, and J. Kwok, eds., Lecture Notes in Computer Science, Berlin, Heidelberg: Springer Berlin Heidelberg, 2011, pp.481-489.
    PUB | DOI
     
  • [237]
    2011 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982111
    B. Hammer, et al., “Prototype-Based Classification of Dissimilarity Data”, Advances in Intelligent Data Analysis X, J. Gama, E. Bradley, and J. Hollmén, eds., Lecture Notes in Computer Science, Berlin, Heidelberg: Springer Berlin Heidelberg, 2011, pp.185-197.
    PUB | DOI
     
  • [236]
    2011 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982110
    F.-M. Schleif, A. Gisbrecht, and B. Hammer, “Accelerating Kernel Neural Gas”, Artificial Neural Networks and Machine Learning – ICANN 2011, T. Honkela, et al., eds., Lecture Notes in Computer Science, Berlin, Heidelberg: Springer Berlin Heidelberg, 2011, pp.150-158.
    PUB | DOI
     
  • [235]
    2011 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982109
    B. Hammer, et al., “A General Framework for Dimensionality Reduction for Large Data Sets”, Advances in Self-Organizing Maps, J. Laaksonen and T. Honkela, eds., Lecture Notes in Computer Science, Berlin, Heidelberg: Springer Berlin Heidelberg, 2011, pp.277-287.
    PUB | DOI
     
  • [234]
    2011 | Preprint | Veröffentlicht | PUB-ID: 2534994
    F.-M. Schleif, A. Gisbrecht, and B. Hammer, “Supervised learning of short and high-dimensional temporal sequences for life science measurements”, 2011.
    PUB | arXiv
     
  • [233]
    2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276480
    A. Gisbrecht, et al., “Linear time heuristics for topographic mapping of dissimilarity data”, 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, vol. 6936, Berlin, Heidelberg: Springer, 2011, pp.25-33.
    PUB | DOI
     
  • [232]
    2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276485
    B. Hammer, et al., “Topographic Mapping of Dissimilarity Data”, WSOM'11, 2011.
    PUB
     
  • [231]
    2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276492
    F.-M. Schleif, A. Gisbrecht, and B. Hammer, “Accelerating Kernel Neural Gas”, ICANN'2011, S. Kaski, et al., eds., 2011.
    PUB
     
  • [230]
    2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276500
    M. Kaestner, et al., “Generalized Functional Relevance Learning Vector Quantization”, European Symposium on Artificial Neural Networks, M. Verleysen, ed., D side, 2011, pp.pp. 93-98.
    PUB
     
  • [229]
    2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276512
    B. Hammer, et al., “A general framework for dimensionality reduction for large data sets”, WSOM'11, 2011.
    PUB
     
  • [228]
    2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276517
    K. Bunte, M. Biehl, and B. Hammer, “Supervised dimension reduction mappings”, European Symposium on Artificial Neural Networks, M. Verleysen, ed., D side, 2011, pp.pp. 281-286.
    PUB
     
  • [227]
    2011 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2276531
    A. Gisbrecht, B. Mokbel, and B. Hammer, “Relational Generative Topographic Mapping”, Neurocomputing, vol. 74, 2011, pp. 1359-1371.
    PUB | DOI | WoS
     
  • [226]
    2011 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2276506
    K. Bunte, et al., “Neighbor embedding XOM for dimension reduction and visualization”, Neurocomputing, vol. 74, 2011, pp. 1340-1350.
    PUB | DOI | WoS
     
  • [225]
    2011 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2309980
    F.-M. Schleif, et al., “Efficient Kernelized Prototype-based Classification”, International Journal of Neural Systems, vol. 21, 2011, pp. 443-457.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [224]
    2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276522
    A. Gisbrecht, et al., “Accelerating dissimilarity clustering for biomedical data analysis”, IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, 2011, pp.pp.154-161.
    PUB
     
  • [223]
    2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276527
    K. Bunte, M. Biehl, and B. Hammer, “Dimensionality Reduction Mappings”, IEEE Symposium on Computational Intelligence and Data Mining, IEEE Computational Intelligence Society, ed., Piscataway, NJ: IEEE, 2011, pp.pp. 349-356.
    PUB | DOI
     
  • [222]
    2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2091665
    X. Zhu and B. Hammer, “Patch Affinity Propagation”, Presented at the 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, Louvain-la-Neuve: Ciaco - i6doc.com, 2011.
    PUB
     
  • [221]
    2011 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993288
    B. Arnonkijpanich, A. Hasenfuss, and B. Hammer, “Local matrix adaptation in topographic neural maps”, Neurocomputing, vol. 74, 2011, pp. 522-539.
    PUB | DOI | WoS
     
  • [220]
    2011 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2276540
    A. Gisbrecht and B. Hammer, “Relevance learning in generative topographic mapping”, Neurocomputing, vol. 74, 2011, pp. 1351-1358.
    PUB | DOI | WoS
     
  • [219]
    2010 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982117
    A. Gisbrecht, et al., “Visualizing Dissimilarity Data Using Generative Topographic Mapping”, KI 2010: Advances in Artificial Intelligence, R. Dillmann, et al., eds., Lecture Notes in Computer Science, Berlin, Heidelberg: Springer Berlin Heidelberg, 2010, pp.227-237.
    PUB | DOI
     
  • [218]
    2010 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982116
    T. Villmann, et al., “The Mathematics of Divergence Based Online Learning in Vector Quantization”, Artificial Neural Networks in Pattern Recognition, F. Schwenker and N. El Gayar, eds., Lecture Notes in Computer Science, Berlin, Heidelberg: Springer Berlin Heidelberg, 2010, pp.108-119.
    PUB | DOI
     
  • [217]
    2010 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982115
    B. Arnonkijpanich and B. Hammer, “Global Coordination Based on Matrix Neural Gas for Dynamic Texture Synthesis”, Artificial Neural Networks in Pattern Recognition. 4th IAPR TC3 Workshop, ANNPR 2010, Cairo, Egypt, April 11-13, 2010. Proceedings, F. Schwenker and N. El Gayar, eds., Lecture Notes in Computer Science, Berlin, Heidelberg: Springer Berlin Heidelberg, 2010, pp.84-95.
    PUB | DOI
     
  • [216]
    2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2982114
    A. Haupt, et al., “Automated generation of classifier based monitoring functions and its application to automotive steering control”, IFAC Proceedings Volumes, vol. 43, 2010, pp. 721-726.
    PUB | DOI
     
  • [215]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276543
    A. Gisbrecht, B. Mokbel, and B. Hammer, “The Nystrom approximation for relational generative topographic mappings”, NIPS workshop on challenges of Data Visualization, 2010.
    PUB
     
  • [214]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994127
    T. Villmann, et al., “Divergence Based Online Learning in Vector Quantization”, Artificial Intelligence and Soft Computing. Lecture Notes in Computer Science, 6113, L. Rutkowski, et al., eds., Berlin, Heidelberg: Springer, 2010, pp.479-486.
    PUB | DOI
     
  • [213]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1796018
    B. Arnonkijpanich, A. Hasenfuss, and B. Hammer, “Local matrix learning in clustering and applications for manifold visualization”, Neural Networks, vol. 23, 2010, pp. 476-486.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [212]
    2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1796195
    P. Schneider, M. Biehl, and B. Hammer, “Hyperparameter learning in probabilistic prototype-based models”, Neurocomputing, vol. 73, 2010, pp. 1117-1124.
    PUB | DOI | WoS
     
  • [211]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993273
    B. Arnonkijpanich and B. Hammer, “Global Coordination based on Matrix Neural Gas for Dynamic Texture Synthesis”, ANNPR'2010. Lecture Notes in Artificial Intelligence, 5998, N. El Gayar and F. Schwenker, eds., Springer, 2010, pp.84-95.
    PUB
     
  • [210]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993367
    K. Bunte, et al., “Exploratory Observation Machine (XOM) with Kullback-Leibler Divergence for Dimensionality Reduction and Visualization”, ESANN'10. Proceedings of the 18th European Symposium on Artificial Neural Networks, M. Verleysen, ed., Evere: D side, 2010, pp.87-92.
    PUB
     
  • [209]
    2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1929672
    A.W. Witoelar, et al., “Window-Based Example Selection in Learning Vector Quantization”, Neural Computing, vol. 22, 2010, pp. 2924-2961.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [208]
    2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1796189
    K. Bunte, et al., “Adaptive local dissimilarity measures for discriminative dimension reduction of labeled data”, Neurocomputing, vol. 73, 2010, pp. 1074-1092.
    PUB | DOI | WoS
     
  • [207]
    2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1794373
    B. Hammer and A. Hasenfuss, “Topographic Mapping of Large Dissimilarity Data Sets”, Neural Computation, vol. 22, 2010, pp. 2229-2284.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [206]
    2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1795962
    P. Schneider, et al., “Regularization in Matrix Relevance Learning”, IEEE Transactions on Neural Networks, vol. 21, 2010, pp. 831-840.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [205]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993978
    F.-M. Schleif, et al., “Generalized derivative based Kernelized learning vector quantization”, Intelligent Data Engineering and Automated Learning – IDEAL 2010 11th International Conference, Paisley, UK, September 1-3, 2010. Proceedings, C. Fyfe, et al., eds., Berlin u.a.: Springer, 2010, pp.21-28.
    PUB | DOI
     
  • [204]
    2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994034
    S. Simmuteit, et al., “Evolving trees for the retrieval of mass spectrometry-based bacteria fingerprints”, Knowledge and Information Systems, vol. 25, 2010, pp. 327-343.
    PUB | DOI | WoS
     
  • [203]
    2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993435
    T. Geweniger, et al., “Median fuzzy-c-means for clustering dissimilarity data”, Neurocomputing, vol. 73, 2010, pp. 1109-1116.
    PUB | DOI | WoS
     
  • [202]
    2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993466
    M. Gori, et al., “Perspectives and challenges for recurrent neural network training”, Logic Journal of the IGPL, vol. 18, 2010, pp. 617-619.
    PUB | DOI | WoS
     
  • [201]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993536
    B. Hammer and A. Hasenfuss, “Clustering very large dissimilarity data sets”, Artificial Neural Networks in Pattern Recognition (ANNPR 2010). Proceedings, F. Schwenker and N. El Gayar, eds., Lecture Notes in Artificial Intelligence, vol. 5998, Berlin: Springer, 2010, pp.259-273.
    PUB | DOI
     
  • [200]
    2010 | Konferenzband | Veröffentlicht | PUB-ID: 2276535
    B. Hammer, et al., eds., Learning paradigms in dynamic environments, 25.07.10-30.07.20, vol. 10302, Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany, 2010.
    PUB
     
  • [199]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276547
    B. Mokbel, A. Gisbrecht, and B. Hammer, “On the effect of clustering on quality assessment measures for dimensionality reduction”, NIPS workshop on Challenges of Data Visualization, 2010.
    PUB
     
  • [198]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993448
    A. Gisbrecht and B. Hammer, “Relevance learning in generative topographic maps”, ESANN'10, M. Verleysen, ed., Evere: D side, 2010, pp.387-392.
    PUB
     
  • [197]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993452
    A. Gisbrecht, B. Mokbel, and B. Hammer, “Relational Generative Topographic Map”, ESANN'10, M. Verleysen, ed., Evere: D side, 2010, pp.277-282.
    PUB
     
  • [196]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993457
    A. Gisbrecht, et al., “Visualizing Dissimilarity Data using generative topographic mapping”, KI'2010, R. Dillmann, et al., eds., 2010, pp.227-237.
    PUB
     
  • [195]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994138
    T. Villmann, et al., “The Mathematics of Divergence Based Online Learning in Vector Quanitzation”, ANNPR'2010, N. El Gayar and F. Schwenker, eds., Berlin, Heidelberg: Springer, 2010, pp.108-119.
    PUB
     
  • [194]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994227
    T. Villmann, F.-M. Schleif, and B. Hammer, “Sparse representation of data”, ESANN'10, M. Verleysen, ed., D side, 2010, pp.225-234.
    PUB
     
  • [193]
    2009 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982118
    T. Villmann and B. Hammer, “Functional Principal Component Learning Using Oja’s Method and Sobolev Norms”, Advances in Self-Organizing Maps. 7th International Workshop, WSOM 2009, St. Augustine, FL, USA, June 8-10, 2009. Proceedings, J.C. Príncipe and R. Miikkulainen, eds., Lecture Notes in Computer Science, Berlin, Heidelberg: Springer Berlin Heidelberg, 2009, pp.325-333.
    PUB | DOI
     
  • [192]
    2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993679
    B. Hammer, B. Schrauwen, and J.J. Steil, “Recent advances in efficient learning of recurrent networks”, European Symposium on Artificial Neural Networks, M. Verleysen, ed., Brugge: d-facto, 2009, pp.213-226.
    PUB
     
  • [191]
    2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993984
    F.-M. Schleif, et al., “Cancer Informatics by Prototype-networks in Mass Spectrometry”, Artificial Intelligence in Medicine, vol. 45, 2009, pp. 215-228.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [190]
    2009 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1994160
    T. Villmann, B. Hammer, and M. Biehl, “Some theoretical aspects of the neural gas vector quantizer”, Similarity Based Clustering, M. Biehl, et al., eds., Lecture Notes Artificial Intelligence, 5400, Berlin, Heidelberg: Springer, 2009, pp.23-34.
    PUB | DOI
     
  • [189]
    2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994305
    A. Witolaer, M. Biehl, and B. Hammer, “Equilibrium properties of offline LVQ”, European Symposium on Artificial Neural Networks, M. Verleysen, ed., d-side publications, 2009, pp.535-540.
    PUB
     
  • [188]
    2009 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1993326
    M. Biehl, et al., “Metric learning for prototype based classification”, Innovations in Neural Information – Paradigms and Applications, M. Bianchini, M. Maggini, and F. Scarselli, eds., Studies in Computational Intelligence, 247, Berlin: Springer, 2009, pp.183-199.
    PUB | DOI
     
  • [187]
    2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993422
    T. Geweniger, et al., “Fuzzy variant of affinity propagation in comparison to median fuzzy c-means”, Advances in Self-Organizing Maps, J.C. Principe and R. Miikkulainen, eds., 2009, pp.72-79.
    PUB | DOI
     
  • [186]
    2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993994
    P. Schneider, M. Biehl, and B. Hammer, “Hyperparameter Learning in robust soft LVQ”, European Symposium on Artificial Neural Networks, M. Verleysen, ed., d-side publications, 2009, pp.517-522.
    PUB
     
  • [185]
    2009 | Konferenzband | Veröffentlicht | PUB-ID: 1994310
    M. Biehl, et al., eds., Similarity-based learning on structures, vol. 9081, Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany, 2009.
    PUB
     
  • [184]
    2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994008
    P. Schneider, M. Biehl, and B. Hammer, “Distance learning in discriminative vector quantization”, Neural Computation, vol. 21, 2009, pp. 2942-2969.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [183]
    2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993269
    N. Alex, A. Hasenfuss, and B. Hammer, “Patch Clustering for Massive Data Sets”, Neurocomputing, vol. 72, 2009, pp. 1455-1469.
    PUB | DOI | WoS
     
  • [182]
    2009 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1993555
    B. Hammer, A. Hasenfuss, and F. Rossi, “Median topographic maps for biological data sets”, Similarity Based Clustering, M. Biehl, et al., eds., Lecture Notes Artificial Intelligence, 5400, Berlin, Heidelberg: Springer, 2009, pp.92-117.
    PUB | DOI
     
  • [181]
    2009 | Report | Veröffentlicht | PUB-ID: 1993316
    M. Biehl, et al., Stationarity of Matrix Relevance Learning Vector Quantization, Machine Learning Reports, Leipzig: Universität Leipzig, 2009.
    PUB
     
  • [180]
    2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993361
    K. Bunte, B. Hammer, and M. Biehl, “Nonlinear dimension reduction and visualization of labeled data”, International Conference on Computer Analysis of Images and Patterns, X. Jiang and N. Petkov, eds., Lecture Notes in Computer Science, 5702, vol. 5702, Berlin: Springer, 2009, pp.1162-1170.
    PUB | DOI
     
  • [179]
    2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993429
    T. Geweniger, et al., “Median variant of fuzzy-c-means”, European Symposium on Artificial Neural Networks, M. Verleysen, ed., Evere: d-side publications, 2009, pp.523-528.
    PUB
     
  • [178]
    2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993835
    B. Mokbel, A. Hasenfuss, and B. Hammer, “Graph-based Representation of Symbolic Musical Data”, Graph-Based Representation in Pattern Recognition (GbRPR 2009). Lecture Notes in Computer Science, 5534, A. Torsello, et al., eds., Lecture notes in computer science, vol. 5534, Berlin: Springer, 2009, pp.42-51.
    PUB | DOI
     
  • [177]
    2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994004
    P. Schneider, M. Biehl, and B. Hammer, “Adaptive relevance matrices in learning vector quantization”, Neural Computation, vol. 21, 2009, pp. 3532-3561.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [176]
    2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993356
    K. Bunte, M. Biehl, and B. Hammer, “Nonlinear discriminative data visualization”, European Symposium on Artificial Neural Networks, M. Verleysen, ed., Evere: d-side publications, 2009, pp.65-70.
    PUB
     
  • [175]
    2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994152
    T. Villmann and B. Hammer, “Functional principal component learning using Oja's method and Sobolev norms”, Advances in Self-Organizing Maps, J.C. Principe and R. Miikkulainen, eds., 2009, pp.325-333.
    PUB
     
  • [174]
    2009 | Herausgeber*in Sammelwerk | Veröffentlicht | PUB-ID: 1994316
    M. Biehl, et al., eds., Similarity Based Clustering, Springer Lecture Notes Artificial Intelligence, 5400, Berlin, Heidelberg: Springer, 2009.
    PUB | DOI
     
  • [173]
    2008 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982119
    B. Arnonkijpanich, et al., “Matrix Learning for Topographic Neural Maps”, Artificial Neural Networks - ICANN 2008, V. Kůrková, R. Neruda, and J. Koutník, eds., Lecture Notes in Computer Science, Berlin, Heidelberg: Springer Berlin Heidelberg, 2008, pp.572-582.
    PUB | DOI
     
  • [172]
    2008 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1993939
    F.-M. Schleif, T. Villmann, and B. Hammer, “Pattern Recognition by Supervised Relevance Neural Gas and its Application to Spectral Data in Bioinformatics”, Encyclopedia of Artificial Intelligence, J.R.-n R.-al Dopico, J. Dorado, and A. Pazos, eds., IGI Global, 2008, pp.1337-1342.
    PUB
     
  • [171]
    2008 | Konferenzband | Veröffentlicht | PUB-ID: 1994329
    L. de Raedt, et al., eds., Recurrent Neural Networks - Models, Capacities, and Applications, vol. 8041, Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), 2008.
    PUB
     
  • [170]
    2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993282
    B. Arnonkijpanich, et al., “Matrix Learning for Topographic Neural Maps”, ICANN (1). Lecture Notes in Computer Science, 5163, V. Kurková, R. Neruda, and J. Koutn'ık, eds., Berlin: Springer, 2008, pp.572-582.
    PUB
     
  • [169]
    2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993261
    N. Alex and B. Hammer, “Parallelizing single pass patch clustering”, European Symposium on Artificial Neural Networks, M. Verleysen, ed., Evere, Belgium: d-side publications, 2008, pp.227-232.
    PUB
     
  • [168]
    2008 | Report | Veröffentlicht | PUB-ID: 1993278
    B. Arnonkijpanich, B. Hammer, and A. Hasenfuss, Local Matrix Adaptation in Topographic Neural Maps, IfI-08-07, Clausthal-Zellerfeld: Clausthal University of Technology, 2008.
    PUB
     
  • [167]
    2008 | Report | Veröffentlicht | PUB-ID: 1993379
    K. Bunte, et al., Discriminative Visualization by Limited Rank Matrix Learning, Machine Learning Reports, Leipzig: Universität Leipzig, 2008.
    PUB
     
  • [166]
    2008 | Report | Veröffentlicht | PUB-ID: 1994012
    P. Schneider, M. Biehl, and B. Hammer, Matrix Adaptation in Discriminative Vector Quantization, IfI Technical Report Seriess, Clausthal-Zellerfeld: Clausthal University of Technology, 2008.
    PUB
     
  • [165]
    2008 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994290
    A. Witoelar, et al., “Learning dynamics and robustness of vector quantization and neural gas”, Neurocomputing, vol. 71, 2008, pp. 1210-1219.
    PUB | DOI | WoS
     
  • [164]
    2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993776
    A. Hasenfuss, W. Boerger, and B. Hammer, “Topographic processing of very large text datasets”, Smart Systems Engineering: Computational Intelligence in Architecting Systes (ANNIE 2008), C.H. Daglie, ed., ASME Press, 2008, pp.525-532.
    PUB | DOI
     
  • [163]
    2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993788
    A. Hasenfuss and B. Hammer, “Single Pass Clustering and Classification of Large Dissimilarity Datasets”, Artificial Intelligence and Pattern Recognition, B. Prasad, et al., eds., ISRST, 2008, pp.219-223.
    PUB
     
  • [162]
    2008 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993966
    F.-M. Schleif, T. Villmann, and B. Hammer, “Prototype based Fuzzy Classification in Clinical Proteomics”, International Journal of Approximate Reasoning, vol. 47, 2008, pp. 4-16.
    PUB | DOI | WoS
     
  • [161]
    2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994072
    M. Strickert, et al., “Discriminatory Data Mapping by Matrix-Based Supervised Learning Metrics”, Artificial Neural Networks in Pattern Recognition. Third IAPR Workshop. Proceedings, L. Prevost, S. Marinai, and F. Schwenker, eds., Lecture Notes in Computer Science, 5064, Berlin: Springer, 2008, pp.78-89.
    PUB | DOI
     
  • [160]
    2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994089
    M. Strickert, et al., “Robust Centroid-Based Clustering using Derivatives of Pearson Correlation”, BIOSIGNALS (2), P. Encarnação and A. Veloso, eds., INSTICC - Institute for Systems and Technologies of Information, Control and Communication, 2008, pp.197-203.
    PUB
     
  • [159]
    2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994281
    T. Winkler, et al., “Thinning Mesh Animations”, Proceedings of Vision, Modeling, and Visualization 2008, O. Deussen, D. Keim, and D. Saupe, eds., Konstanz, Germany: Aka, 2008, pp.149-158.
    PUB
     
  • [158]
    2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993804
    A. Hasenfuss, B. Hammer, and F. Rossi, “Patch Relational Neural Gas - Clustering of Huge Dissimilarity Datasets”, Artificial Neural Networks in Pattern Recognition, Third IAPR Workshop. Proceedings. Lecture Notes in Computer Science, 5064, L. Prevost, S. Marinai, and F. Schwenker, eds., Berlin: Springer, 2008, pp.1-12.
    PUB | DOI
     
  • [157]
    2008 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1993900
    F.-M. Schleif, B. Hammer, and T. Villmann, “Analysis of Spectral Data in Clinical Proteomics by use of Learning Vector Quantizers”, Computational Intelligence in Biomedicine and Bioinformatics: Current Trends and Applications, M. Van de Werff, A. Delder, and R. Tollenaar, eds., Berlin: Springer, 2008, pp.141-167.
    PUB | DOI
     
  • [156]
    2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993798
    A. Hasenfuss, et al., “Magnification Control in Relational Neural Gas”, European Symposium on Artificial Neural Networks, M. Verleysen, ed., Brussels: d-side publications, 2008, pp.325-330.
    PUB
     
  • [155]
    2008 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994253
    T. Villmann, et al., “Classification of mass-spectrometric data in clinical proteomics using learning vector quantization methods”, Briefings in Bioinformatics, vol. 9, 2008, pp. 129-143.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [154]
    2008 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2017617
    T. Villmann, et al., “Fuzzy Classification Using Information Theoretic Learning Vector Quantization”, Neurocomputing, vol. 71, 2008, pp. 3070-3076.
    PUB | DOI | WoS
     
  • [153]
    2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2001836
    T. Geweniger, et al., “Comparison of cluster algorithms for the analysis of text data using Kolmogorov complexity”, ICONIP 2008, M. Köppen, N.K. Kasabov, and G.G. Coghill, eds., Berlin, Heidelberg: Springer, 2008, pp.61-69.
    PUB | DOI
     
  • [152]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993848 OA
    F. Rossi, A. Hasenfuß, and B. Hammer, “Accelerating Relational Clustering Algorithms With Sparse Prototype Representation”, Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007), Bielefeld: Bielefeld University, 2007.
    PUB | PDF | DOI
     
  • [151]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994016 OA
    P. Schneider, et al., “Advanced metric adaptation in Generalized LVQ for classification of mass spectrometry data”, Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007), Bielefeld: Bielefeld University, 2007.
    PUB | PDF | DOI
     
  • [150]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994267 OA
    T. Villmann, et al., “Class imaging of hyperspectral satellite remote sensing data using FLSOM”, Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007), Bielefeld: Bielefeld University, 2007.
    PUB | PDF | DOI
     
  • [149]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994295 OA
    A. Witoelar, M. Biehl, and B. Hammer, “Learning Vector Quantization: generalization ability and dynamics of competing prototypes”, Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007), Bielefeld: Bielefeld University, 2007.
    PUB | PDF | DOI
     
  • [148]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993265 OA
    N. Alex, B. Hammer, and F. Klawonn, “Single pass clustering for large data sets”, Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007), Bielefeld: Bielefeld University, 2007.
    PUB | PDF | DOI
     
  • [147]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993563 OA
    B. Hammer, et al., “Topographic Processing of Relational Data”, Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007), Bielefeld: Bielefeld University, 2007.
    PUB | PDF | DOI
     
  • [146]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993547
    B. Hammer, et al., “Intuitive Clustering of Biological Data”, Proceedings of International Joint Conference on Neural Networks, IEEE, 2007, pp.1877-1882.
    PUB | DOI
     
  • [145]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993782
    A. Hasenfuss and B. Hammer, “Relational topographic maps”, Advances in Intelligent Data Analysis VII, Proceedings of the 7th International Symposium on Intelligent Data Analysis, M.R. Berthold, J. Shawe-Taylor, and N. Lavrac, eds., vol. 4723, Berlin: Springer, 2007, pp.93-105.
    PUB | DOI
     
  • [144]
    2007 | Report | Veröffentlicht | PUB-ID: 1993922
    F.-M. Schleif, A. Hasenfuss, and B. Hammer, Aggregation of multiple peak lists by use of an improved neural gas network, Leipzig: Universität Leipzig, 2007.
    PUB
     
  • [143]
    2007 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993297
    M. Biehl, A. Ghosh, and B. Hammer, “Dynamics and generalization ability of LVQ algorithms”, Journal of Machine Learning Research, vol. 8, 2007, pp. 323-360.
    PUB
     
  • [142]
    2007 | Report | Veröffentlicht | PUB-ID: 1993533
    B. Hammer and A. Hasenfuss, Relational topographic Maps, IfI Technical reports, Clausthal-Zellerfeld: Clausthal University of Technology, 2007.
    PUB
     
  • [141]
    2007 | Report | Veröffentlicht | PUB-ID: 1993831
    M. Melato, B. Hammer, and K. Hormann, Neural Gas for Surface Reconstruction, IfI Technical reports, Clausthal-Zellerfeld: Clausthal University of Technology, 2007.
    PUB
     
  • [140]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993970
    F.-M. Schleif, T. Villmann, and B. Hammer, “Analysis of Proteomic Spectral Data by Multi Resolution Analysis and Self-Organizing-Maps”, Application of Fuzzy Sets Theory. Proceedings of the 7th International Workshop on Fuzzy Logic and Applications. LNAI 4578, F. Masulli, S. Mitra, and G. Pasi, eds., Berlin, Heidelberg: Springer, 2007, pp.563-570.
    PUB | DOI
     
  • [139]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993999
    P. Schneider, M. Biehl, and B. Hammer, “Relevance matrices in LVQ”, Proc. Of European Symposium on Artificial Neural Networks, M. Verleysen, ed., Brussels, Belgium: d-side publications, 2007, pp.37-42.
    PUB
     
  • [138]
    2007 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993911
    F.-M. Schleif, B. Hammer, and T. Villmann, “Margin based Active Learning for LVQ Networks”, Neurocomputing, vol. 70, 2007, pp. 1215-1224.
    PUB | DOI | WoS
     
  • [137]
    2007 | Report | Veröffentlicht | PUB-ID: 1993334
    J. Blazewicz, K. Ecker, and B. Hammer, 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.
    PUB
     
  • [136]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993746
    B. Hammer and T. Villmann, “How to process uncertainty in machine learning”, Proc. Of European Symposium on Artificial Neural Networks (ESANN'2007), M. Verleysen, ed., Brussels, Belgium: d-side publications, 2007, pp.79-90.
    PUB
     
  • [135]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993811
    A. Hasenfuss, et al., “Neural gas clustering for dissimilarity data with continuous prototypes”, Computational and Ambient Intelligence – Proceedings of the 9th Work-conference on Artificial Neural Networks. LNCS 4507, F. Sandoval, et al., eds., Berlin: Springer, 2007, pp.539-546.
    PUB | DOI
     
  • [134]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994299
    A. Witolaer, et al., “On the dynamics of vector quantization and neural gas”, Proc. Of European Symposium on Artificial Neural Networks (ESANN'2007), M. Verleysen, ed., Brussels, Belgium: d-side publications, 2007, pp.127-132.
    PUB
     
  • [133]
    2007 | Konferenzband | Veröffentlicht | PUB-ID: 1994321
    M. Biehl, et al., eds., Similarity-based Clustering and its Application to Medicine and Biology, vol. 7131, Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), 2007.
    PUB
     
  • [132]
    2007 | Herausgeber*in Sammelwerk | Veröffentlicht | PUB-ID: 1994326
    B. Hammer and P. Hitzler, eds., Perspectives of Neural-Symbolic Integration, Studies in Computational Intelligence, 77, Berlin: Springer, 2007.
    PUB | DOI
     
  • [131]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993541
    B. Hammer and A. Hasenfuss, “Relational Neural Gas”, KI 2007: Advances in Artificial Intelligence. Lecture Notes in Artificial Intelligence, 4667, J. Hertzberg, M. Beetz, and R. Englert, eds., Berlin: Springer, 2007, pp.190-204.
    PUB | DOI
     
  • [130]
    2007 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993616
    B. Hammer, A. Hasenfuss, and T. Villmann, “Magnification control for batch neural gas”, Neurocomputing, vol. 70, 2007, pp. 1225-1234.
    PUB | DOI | WoS
     
  • [129]
    2007 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1993630
    B. Hammer, A. Micheli, and A. Sperduti, “Adaptive Contextual Processing of Structured Data by Recursive Neural Networks: A Survey of Computational Properties”, Perspectives of Neural-Symbolic Integration, B. Hammer and P. Hitzler, eds., Studies in computational Intelligence, 77, Berlin: Springer, 2007, pp.67-94.
    PUB | DOI
     
  • [128]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993820
    A. Hasenfuss, et al., “Neural gas clustering for sparse proximity data”, Proceedings of the 9th International Work-Conference on Artificial Neural Networks.LNCS 4507, F. Sandoval, et al., eds., Berlin, Heidelberg, Germany: Springer, 2007, pp.539-546.
    PUB
     
  • [127]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993907
    F.-M. Schleif, B. Hammer, and T. Villmann, “Supervised Neural Gas for Functional Data and its Application to the Analysis of Clinical Proteom Spectra”, Computational and Ambient Intelligence. Proceedings of the 9th International Work-Conference on Artificial Neural Networks. LNCS, 4507, F. Sandoval, et al., eds., Berlin, Heidelberg: Springer, 2007, pp.1036-1044.
    PUB | DOI
     
  • [126]
    2007 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1994102
    P. Tino, B. Hammer, and M. Boden, “Markovian Bias of Neural-based Architectures With Feedback Connections”, Perspectives of Neural-Symbolic Integration, B. Hammer and P. Hitzler, eds., Studies in computational Intelligence, 77, Berlin: Springer, 2007, pp.95-134.
    PUB | DOI
     
  • [125]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994258
    T. Villmann, et al., “Fuzzy Labeled Self Organizing Map for Clasification of Spectra”, Computational and Ambient Intelligence. Proceedings of the 9th Work-conference on Artificial Neural Networks. LNCS, 4507, F. Sandoval, et al., eds., Berlin: Springer, 2007, pp.556-563.
    PUB | DOI
     
  • [124]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993578
    B. Hammer, et al., “Supervised Batch Neural Gas”, Proceedings of Conference Artificial Neural Networks in Pattern Recognition (ANNPR), F. Schwenker, ed., Berlin: Springer Verlag, 2006, pp.33-45.
    PUB | DOI
     
  • [123]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993895
    F.-M. Schleif, B. Hammer, and T. Villmann, “Margin based Active Learning for LVQ Networks”, Proc. Of European Symposium on Artificial Neural Networks, M. Verleysen, ed., Brussels, Belgium: d-side publications, 2006, pp.539-544.
    PUB
     
  • [122]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994184
    T. Villmann, et al., “Prototype based classification using information theoretic learning”, Neural Information Processing, 13th International Conference. Proceedings, I. King, et al., eds., Lecture Notes in Computer Science, 4233, vol. Part II, Berlin: Springer, 2006, pp.40-49.
    PUB
     
  • [121]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994273
    T. Villmann, et al., “Fuzzy Labeled Self-Organizing Map with Label-Adjusted Prototypes”, Proceedings of Conference Artificial Neural Networks in Pattern Recognition, F. Schwenker, ed., Berlin: Springer, 2006, pp.46-56.
    PUB | DOI
     
  • [120]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993889
    F.-M. Schleif, et al., “Machine Learning and Soft-Computing in Bioinformatics. A Short Journey”, Proc. of FLINS 2006, World Scientific Press, 2006, pp.541-548.
    PUB
     
  • [119]
    2006 | Report | Veröffentlicht | PUB-ID: 1993322
    M. Biehl, B. Hammer, and P. Schneider, Matrix Learning in Learning Vector Quantization, Clausthal-Zellerfeld: Clausthal University of Technology, 2006.
    PUB
     
  • [118]
    2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993391
    M. Cottrell, et al., “Batch and Median Neural Gas”, Neural Networks, vol. 19, 2006, pp. 762-771.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [117]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993568
    B. Hammer, et al., “Supervised median neural gas”, Smart Engineering System Design. Intelligent Engineering Systems Through Artificial Neural Networks, 16, C. Dagli, et al., eds., ASME Press, 2006, pp.623-633.
    PUB
     
  • [116]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993594
    B. Hammer, et al., “Supervised median clustering”, 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), C.H. Dagli, ed., ASME Press series on intelligent engineering systems through artificial neural networks, 16, New York, NY: ASME Press, 2006, pp.623-632.
    PUB
     
  • [115]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993878
    F.-M. Schleif, et al., “Analysis and Visualization of Proteomic Data by Fuzzy labeled Self-Organizing Maps”, 19th IEEE International Symposium on Computer- based Medical Systems, D.J. Lee, et al., eds., Los Alamitos: IEEE Computer Society Press, 2006, pp.919-924.
    PUB | DOI
     
  • [114]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994028
    U. Seiffert, et al., “Neural Networks and Machine Learning in Bioinformatics - Theory and Applications”, Proc. Of European Symposium on Artificial Neural Networks, M. Verleysen, ed., Brussels, Belgium: d-side publications, 2006, pp.521-532.
    PUB
     
  • [113]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994201
    T. Villmann, B. Hammer, and U. Seiffert, “Perspectives of Self-adapted Self-organizing Clustering in Organic Computing”, Biologically Inspired Approaches to Advanced Information Technology, Second International Workshop. Proceedings. Lecture Notes in Computer Science, 3853, A.J. Ijspeert, T. Masuzawa, and S. Kusumoto, eds., Berlin: Springer, 2006, pp.141-159.
    PUB | DOI
     
  • [112]
    2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994237
    T. Villmann, F.-M. Schleif, and B. Hammer, “Comparison of relevance learning vector quantization with other metric adaptive classification methods”, Neural Networks, vol. 19, 2006, pp. 610-622.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [111]
    2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993301
    M. Biehl, A. Ghosh, and B. Hammer, “Learning vector quantization: The dynamics of winner-takes-all algorithms”, Neurocomputing, vol. 69, 2006, pp. 660-670.
    PUB | DOI | WoS
     
  • [110]
    2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993440
    A. Ghosh, M. Biehl, and B. Hammer, “Performance analysis of LVQ algorithms: a statistical physics approach”, Neural Networks, vol. 19, 2006, pp. 817-829.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [109]
    2006 | Report | Veröffentlicht | PUB-ID: 1993584
    B. Hammer, et al., Supervised median clustering, IfI Technical reports, Clausthal-Zellerfeld: Clausthal University of Technology, 2006.
    PUB
     
  • [108]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993611
    B. Hammer, A. Hasenfuss, and T. Villmann, “Magnification Control for Batch Neural Gas”, Proc. Of European Symposium on Artificial Neural Networks, M. Verleysen, ed., Brussels: d-side publications, 2006, pp.7-12.
    PUB
     
  • [107]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993659
    B. Hammer and N. Neubauer, “On the capacity of unsupervised recursive neural networks for symbol processing”, Workshop proceedings of NeSy'06, A. d'Avila Garcez, P. Hitzler, and G. Tamburrini, eds., 2006.
    PUB
     
  • [106]
    2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993762
    B. Hammer and T. Villmann, “Effizient Klassifizieren und Clustern: Lernparadigmen von Vektorquantisierern”, Künstliche Intelligenz, vol. 3, 2006, pp. 5-11.
    PUB
     
  • [105]
    2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994082
    M. Strickert, et al., “Generalized relevance LVQ (GRLVQ) with correlation measures for gene expression analysis”, Neurocomputing, vol. 69, 2006, pp. 651-659.
    PUB | DOI | WoS
     
  • [104]
    2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994195
    T. Villmann, et al., “Fuzzy Classification by Fuzzy Labeled Neural Gas”, Neural Networks, vol. 19, 2006, pp. 772-779.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [103]
    2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994241
    T. Villmann, F.-M. Schleif, and B. Hammer, “Prototype-based fuzzy classification with local relevance for proteomics”, Neurocomputing, vol. 69, 2006, pp. 2425-2428.
    PUB | DOI | WoS
     
  • [102]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2017225
    B. Hammer, et al., “Learning vector quantization classification with local relevance determination for medical data”, Artificial Intelligence and Soft-Computing - Proceedings of ICAISC 2006. LNAI, 4029, L. Rutkowski, et al., eds., Lecture notes in computer science ; 4029 : Lecture notes in artificial intelligence, vol. 4029, Berlin, Heidelberg: Springer, 2006, pp.603-612.
    PUB | DOI
     
  • [101]
    2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982120
    T. Villmann, F.-M. Schleif, and B. Hammer, “Fuzzy Labeled Soft Nearest Neighbor Classification with Relevance Learning”, Fourth International Conference on Machine Learning and Applications (ICMLA'05), IEEE, 2005, pp.11-15.
    PUB | DOI
     
  • [100]
    2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993624
    B. Hammer, et al., “Self Organizing Maps for Time Series”, Proceedings of WSOM 2005, 2005, pp.115-122.
    PUB
     
  • [99]
    2005 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994057
    M. Strickert and B. Hammer, “Merge SOM for temporal data”, Neurocomputing, vol. 64, 2005, pp. 39-71.
    PUB | DOI | WoS
     
  • [98]
    2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994172
    T. Villmann, et al., “Fuzzy Labeled Neural GAS for Fuzzy Classification”, Proceedings of the 5th Workshop on Self-Organizing Maps [on CD-ROM], M. Cottrell, ed., Paris, France: University Paris-1-Pantheon-Sorbonne, 2005, pp.283-290.
    PUB
     
  • [97]
    2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993305
    M. Biehl, A. Gosh, and B. Hammer, “The dynamics of Learning Vector Quantization”, ESANN'05, M. Verleysen, ed., Evere: d-side publishing, 2005, pp.13-18.
    PUB
     
  • [96]
    2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993386
    M. Cottrell, et al., “Batch NG”, Proceedings of WSOM 2005, 2005, pp.275-282.
    PUB
     
  • [95]
    2005 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993406
    B. DasGupta and B. Hammer, “On approximate learning by multi-layered feedforward circuits”, Theoretical Computer Science, vol. 348, 2005, pp. 95-127.
    PUB | DOI | WoS
     
  • [94]
    2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993444
    A. Ghosh, M. Biehl, and B. Hammer, “Dynamical Analysis of LVQ type learning rules”, Proceedings of WSOM, 2005, pp.578-594.
    PUB
     
  • [93]
    2005 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993641
    B. Hammer, A. Micheli, and A. Sperduti, “Universal approximation capability of cascade correlation for structures”, Neural Computation, vol. 17, 2005, pp. 1109-1159.
    PUB | DOI | WoS
     
  • [92]
    2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993665
    B. Hammer, et al., “Relevance learning for mental disease classification”, ESANN'05, M. Verleysen, ed., d-side publishing, 2005, pp.139-144.
    PUB
     
  • [91]
    2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994118
    K. Tluk von Toschanowitz, B. Hammer, and H. Ritter, “Relevance determination in reinforcement learning”, ESANN'05, M. Verleysen, ed., d-side publishing, 2005, pp.369-374.
    PUB
     
  • [90]
    2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994219
    T. Villmann, F.-M. Schleif, and B. Hammer, “Fuzzy Classification for Classification of Mass Spectrometric Data Based on Learning Vector Quantization”, International Workshop on Integrative Bioinformatics, 2005.
    PUB
     
  • [89]
    2005 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993396
    M. Cottrell, B. Hammer, and T. Villmann, “New Aspects in Neurocomputing.”, Neurocomputing, vol. 63, 2005, pp. 1-3.
    PUB | DOI | WoS
     
  • [88]
    2005 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993416
    K. Gersmann and B. Hammer, “Improving iterative repair strategies for scheduling with the SVM”, Neurocomputing, vol. 63, 2005, pp. 271-292.
    PUB | DOI | WoS
     
  • [87]
    2005 | Report | Veröffentlicht | PUB-ID: 1993675
    B. Hammer, F.-M. Schleif, and T. Villmann, On the Generalization Ability of Prototype-Based Classifiers with Local Relevance Determination, IfI Technical reports, Clausthal-Zellerfeld: Clausthal University of Technology, 2005.
    PUB
     
  • [86]
    2005 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993721
    B. Hammer, M. Strickert, and T. Villmann, “Supervised neural gas with general similarity measure”, Neural Processing Letters, vol. 21, 2005, pp. 21-44.
    PUB | DOI | WoS
     
  • [85]
    2005 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993671
    B. Hammer, C. Saunders, and A. Sperduti, “Special issue on neural networks and kernel methods for structured domains”, Neural Networks, vol. 18, 2005, pp. 1015-1018.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [84]
    2005 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1993710
    B. Hammer, M. Strickert, and T. Villmann, “Prototype based recognition of splice sites”, Bioinformatics using computational intelligence paradigms, U. Seiffert, L.C. Jain, and P. Schweitzer, eds., Berlin: Springer, 2005, pp.25-55.
    PUB
     
  • [83]
    2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993974
    F.-M. Schleif, T. Villmann, and B. Hammer, “Local Metric Adaptation for Soft Nearest Prototype Classification to Classify Proteomic Data”, Proceedings of the 6th Workshop on Fuzzy Logic and Applications, I. Bloch, A. Petrosino, and A.G.B. Tettamanzi, eds., Berlin, Heidelberg: Springer, 2005, pp.290-296.
    PUB | DOI
     
  • [82]
    2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994249
    T. Villmann, F.-M. Schleif, and B. Hammer, “Fuzzy labeled soft nearest neighbor classification with relevance learning”, Proceedings of the International Conference of Machine Learning Applications, M.A. Wani, K.J. Cios, and K. Hafeez, eds., Los Angeles: IEEE Press, 2005, pp.11-15.
    PUB
     
  • [81]
    2005 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993717
    B. Hammer, M. Strickert, and T. Villmann, “On the generalization ability of GRLVQ networks”, Neural Processing Letters, vol. 21, 2005, pp. 109-120.
    PUB | DOI | WoS
     
  • [80]
    2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993750
    B. Hammer and T. Villmann, “Classification using non standard metrics”, ESANN'05, M. Verleysen, ed., Brussels: d-side publishing, 2005, pp.303-316.
    PUB
     
  • [79]
    2005 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994063
    M. Strickert, B. Hammer, and S. Blohm, “Unsupervised recursive sequences processing”, Neurocomputing, vol. 63, 2005, pp. 69-97.
    PUB | DOI | WoS
     
  • [78]
    2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982121
    K. Gersmann and B. Hammer, “A reinforcement learning algorithm to improve scheduling search heuristics with the SVM”, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541), vol. 3, IEEE, 2004, pp.1811-1816.
    PUB | DOI
     
  • [77]
    2004 | Report | Veröffentlicht | PUB-ID: 1993732
    B. Hammer, P. Tino, and A. Micheli, A mathematical characterization of the architectural bias of recursive models, Osnabrücker Schriften zur Mathematik, Osnabrück: Universität Osnabrück, 2004.
    PUB
     
  • [76]
    2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994168
    T. Villmann, B. Hammer, and F.-M. Schleif, “Metrik Adaptation for Optimal Feature Classification in Learning Vector Quantization Applied to Environment Detection”, Proceedings of Selbstorganisation Von Adaptivem Verfahren. Fortschritts-Berichte VDI Reihe 10, Nr. 742, H.-M. Groß, K. Debes, and H.-J. Böhme, eds., VDI Verlag, 2004, pp.592-597.
    PUB
     
  • [75]
    2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994111
    K. Tluk von Toschanowitz, B. Hammer, and H. Ritter, “Mapping the Design Space of Reinforcement Learning Problems - a Case Study”, SOAVE 2004, 3rd Workshop on SelfOrganization of AdaptiVE Behavior, H.-M. Gross, K. Debes, and H.-J. Böhme, eds., VDI Verlag, 2004, pp.251-261.
    PUB
     
  • [74]
    2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994212
    T. Villmann, F.-M. Schleif, and B. Hammer, “Metric adaptation for optimal feature classification in learning vector quantization applied to environment detection”, SOAVE 2004, 3rd Workshop on SelfOrganization of AdaptiVE Behavior, H.-M. Groß, K. Debes, and H.-J. Böhme, eds., VDI Verlag, 2004.
    PUB
     
  • [73]
    2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993620
    B. Hammer and B.J. Jain, “Neural methods for non-standard data”, European Symposium on Artificial Neural Networks'2004, M. Verleysen, ed., D-side publications, 2004, pp.281-292.
    PUB
     
  • [72]
    2004 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993649
    B. Hammer, et al., “Recursive self-organizing network models”, Neural Networks, vol. 17, 2004, pp. 1061-1085.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [71]
    2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993702
    B. Hammer, M. Strickert, and T. Villmann, “Relevance LVQ versus SVM”, Artificial Intelligence and Softcomputing, Lecture Notes in Artificial Intelligence, 3070, L. Rutkowski, et al., eds., Berlin: Springer, 2004, pp.592-597.
    PUB
     
  • [70]
    2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993419
    K. Gersmann and B. Hammer, “A reinforcement learning algorithm to improve scheduling search heuristics with the SVM”, IJCNN, 2004.
    PUB
     
  • [69]
    2004 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993654
    B. Hammer, et al., “A general framework for unsupervised processing of structured data”, Neurocomputing, vol. 57, 2004, pp. 3-35.
    PUB | DOI | WoS
     
  • [68]
    2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993870
    F.-M. Schleif, et al., “Supervised Relevance Neural Gas and Unified Maximum Separability Analysis for Classification of Mass Spectrometric Data”, Proceedings of the 3rd International Conference on Machine Learning and Applications (ICMLA) 2004, M.A. Wani, K.J. Cios, and K. Hafeez, eds., Los Alamitos, CA, USA: IEEE Press, 2004, pp.374-379.
    PUB
     
  • [67]
    2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994049
    M. Strickert and B. Hammer, “Self-organizing context learning”, European Symposium on Artificial Neural Networks, M. Verleysen, ed., D-side publications, 2004, pp.39-44.
    PUB
     
  • [66]
    2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994099
    P. Tino and B. Hammer, “On early stages of learning in connectionist models with feedback connections”, Compositional Connectionism in Cognitive Science, 2004.
    PUB
     
  • [65]
    2003 | Preprint | PUB-ID: 2999891 OA
    B. Hammer, “Mathematical Aspects of Neural Netorks (Habilitation)”, Habilitationsschrift (kumulativ), 2003.
    PUB | PDF
     
  • [64]
    2003 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2982124
    B. Hammer and P. Tiňo, “Recurrent Neural Networks with Small Weights Implement Definite Memory Machines”, Neural Computation, vol. 15, 2003, pp. 1897-1929.
    PUB | DOI
     
  • [63]
    2003 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2982123
    T. Villmann, E. Merényi, and B. Hammer, “Neural maps in remote sensing image analysis”, Neural Networks, vol. 16, 2003, pp. 389-403.
    PUB | DOI
     
  • [62]
    2003 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2982122
    P. Tiňo and B. Hammer, “Architectural Bias in Recurrent Neural Networks: Fractal Analysis”, Neural Computation, vol. 15, 2003, pp. 1931-1957.
    PUB | DOI
     
  • [61]
    2003 | Report | Veröffentlicht | PUB-ID: 1993725
    B. Hammer, M. Strickert, and T. Villmann, On the generalization ability of GRLVQ, Osnabrücker Schriften zur Mathematik, Osnabrück: Universität Osnabrück, 2003.
    PUB
     
  • [60]
    2003 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994108
    P. Tiño and B. Hammer, “Architectural Bias in Recurrent Neural Networks: Fractal Analysis”, Neural Computation, vol. 15, 2003, pp. 1931-1957.
    PUB
     
  • [59]
    2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994223
    T. Villmann, F.-M. Schleif, and B. Hammer, “Supervised Neural Gas and Relevance Learning in Learning Vector Quantization”, Proceedings of the 4th Workshop on Self Organizing Maps [on CD-ROM], T. Yamakawa, ed., Hibikino, Kitakyushu, Japan: Kyushu Institute of Technology, 2003, pp.47-52.
    PUB
     
  • [58]
    2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993338
    T. Bojer, B. Hammer, and C. Koeers, “Monitoring technical systems with prototype based clustering”, ESANN 2003, 10th European Symposium on Artificial Neural Network. Proceedings, M. Verleysen, ed., Evere: D-side publication, 2003, pp.433-439.
    PUB
     
  • [57]
    2003 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993530
    B. Hammer and K. Gersmann, “A Note on the Universal Approximation Capability of Support Vector Machines”, Neural Processing Letters, vol. 17, 2003, pp. 43-53.
    PUB
     
  • [56]
    2003 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1993487
    B. Hammer, “Perspectives on learning symbolic data with connectionistic systems”, Adaptivity and Learning, R. Kühn, et al., eds., Berlin: Springer, 2003, pp.141-160.
    PUB
     
  • [55]
    2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993754
    B. Hammer and T. Villmann, “Mathematical Aspects of Neural Networks”, Proc. Of European Symposium on Artificial Neural Networks (ESANN'2003), M. Verleysen, ed., Brussels, Belgium: d-side, 2003, pp.59-72.
    PUB
     
  • [54]
    2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994053
    M. Strickert and B. Hammer, “Unsupervised recursive sequence processing”, 10th European Symposium on Artificial Neural Networks. Proceedings, M. Verleysen, ed., D-side publication, 2003, pp.27-32.
    PUB
     
  • [53]
    2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994060
    M. Strickert and B. Hammer, “Neural Gas for Sequences”, WSOM'03, 2003, pp.53-57.
    PUB
     
  • [52]
    2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993412
    K. Gersmann and B. Hammer, “Improving iterative repair strategies for scheduling with the SVM”, ESANN 2003, 10th European Symposium on Artificial Neural Networks. Proceedings, M. Verleysen, ed., Evere: D-side publication, 2003, pp.235-240.
    PUB
     
  • [51]
    2003 | Report | Veröffentlicht | PUB-ID: 1993645
    B. Hammer, a. Micheli, and A. Sperduti, A general framework for self-organizing structure processing neural networks, Pisa: Universita di Pisa, Dipartimento die Informatica, 2003.
    PUB
     
  • [50]
    2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993349
    T. Bojer, et al., “Determining Relevant Input Dimensions for the Self-Organizing Map”, Neural Networks and Soft Computing (Proc. ICNNSC 2002), L. Rutkowski and J. Kacprzyk, eds., Physica-Verlag, 2003, pp.388-393.
    PUB
     
  • [49]
    2003 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993736
    B. Hammer and P. Tiño, “Recurrent Neural Networks with Small Weights Implement Definite Memory Machines”, Neural Computation, vol. 15, 2003, pp. 1897-1929.
    PUB
     
  • [48]
    2003 | Report | Veröffentlicht | PUB-ID: 1994157
    T. Villmann and B. Hammer, Metric adaptation and relevance learning in learning vector quantization, Osnabrücker Schriften zur Mathematik, Osnabrück: Universität Osnabrück, 2003.
    PUB
     
  • [47]
    2003 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994208
    T. Villmann, E. Merényi, and B. Hammer, “Neural maps in remote sensing image analysis”, Neural Networks, vol. 16, 2003, pp. 389-403.
    PUB
     
  • [46]
    2002 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2982126
    B. Hammer, “Recurrent networks for structured data – A unifying approach and its properties”, Cognitive Systems Research, vol. 3, 2002, pp. 145-165.
    PUB | DOI
     
  • [45]
    2002 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2982125
    B. Hammer and T. Villmann, “Generalized relevance learning vector quantization”, Neural Networks, vol. 15, 2002, pp. 1059-1068.
    PUB | DOI
     
  • [44]
    2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993636
    B. Hammer, A. Micheli, and A. Sperduti, “A general framework for unsupervised processing of structured data”, ESANN 2002, 10th European Symposium on Artificial Neural Network. Proceedings, M. Verleysen, ed., De-side publication, 2002, pp.389-394.
    PUB
     
  • [43]
    2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994095
    P. Tino and B. Hammer, “Architectural bias in recurrent neural networks – fractal analysis”, Proc. International Conf. on Artificial Neural Networks. Lecture Notes in Computer Science, 2415, J.R. Dorronsoro, ed., Berlin: Springer, 2002, pp.370-376.
    PUB
     
  • [42]
    2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994146
    T. Villmann and B. Hammer, “Supervised Neural Gas for Learning Vector Quantization”, Proc. of the 5th German Workshop on Artificial Life, D. Polani, J. Kim, and T. Martinetz, eds., Berlin: Akademische Verlagsgesellschaft - infix - IOS Press, 2002, pp.9-16.
    PUB
     
  • [41]
    2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993688
    B. Hammer and J.J. Steil, “Perspectives on Learning with Recurrent Neural Networks”, Proc. European Symposium Artificial Neural Networks, M. Verleysen, ed., D-side publication, 2002, pp.357-368.
    PUB
     
  • [40]
    2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993758
    B. Hammer and T. Villmann, “Batch-GRLVQ”, Proc. Of European Symposium on Artificial Neural Networks (ESANN'2002), M. Verleysen, ed., Brussels, Belgium: d-side, 2002, pp.295-300.
    PUB
     
  • [39]
    2002 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993765
    B. Hammer and T. Villmann, “Generalized Relevance Learning Vector Quantization”, Neural Networks, vol. 15, 2002, pp. 1059-1068.
    PUB
     
  • [38]
    2002 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1993471
    B. Hammer, “Compositionality in Neural Systems”, Handbook of Brain Theory and Neural Networks, M. Arbib, ed., 2nd., MIT Press, 2002, pp.244-248.
    PUB
     
  • [37]
    2002 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993508
    B. Hammer, “Recurrent neural networks for structured data – a unifying approach and its properties”, Cognitive Systems Research, vol. 3, 2002, pp. 145-165.
    PUB
     
  • [36]
    2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993692
    B. Hammer, M. Strickert, and T. Villmann, “Learning Vector Quantization for Multimodal Data”, Proc. International Conf. on Artificial Neural Networks (ICANN). Lecture Notes in Computer Science, 2415, J.R. Dorronsoro, ed., Berlin: Springer Verlag, 2002, pp.370-376.
    PUB
     
  • [35]
    2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993697
    B. Hammer, M. Strickert, and T. Villmann, “Rule Extraction from Self-Organizing Networks”, Proc. International Conf. on Artificial Neural Networks (ICANN). Lecture Notes in Computer Science, 2415, J.R. Dorronsoro, ed., Berlin: Springer Verlag, 2002, pp.877-883.
    PUB
     
  • [34]
    2002 | Report | Veröffentlicht | PUB-ID: 1993729
    B. Hammer and P. Tino, Neural networks with small weights implement finite memory machines, Osnabrücker Schriften zur Mathematik, Osnabrück: Universität Osnabrück, 2002.
    PUB
     
  • [33]
    2001 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982130
    B. Hammer, “On the Generalization Ability of Recurrent Networks”, Artificial Neural Networks — ICANN 2001, G. Dorffner, H. Bischof, and K. Hornik, eds., Lecture Notes in Computer Science, vol. 2130, Berlin, Heidelberg: Springer Berlin Heidelberg, 2001, pp.731-736.
    PUB | DOI
     
  • [32]
    2001 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982129
    M. Strickert, T. Bojer, and B. Hammer, “Generalized Relevance LVQ for Time Series”, Artificial Neural Networks — ICANN 2001, G. Dorffner, H. Bischof, and K. Hornik, eds., Lecture Notes in Computer Science, Berlin, Heidelberg: Springer Berlin Heidelberg, 2001, pp.677-683.
    PUB | DOI
     
  • [31]
    2001 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2982128
    B. Hammer, “Generalization ability of folding networks”, IEEE Transactions on Knowledge and Data Engineering, vol. 13, 2001, pp. 196-206.
    PUB | DOI
     
  • [30]
    2001 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2982127
    M. Vidyasagar, S. Balaji, and B. Hammer, “Closure properties of uniform convergence of empirical means and PAC learnability under a family of probability measures”, Systems & Control Letters, vol. 42, 2001, pp. 151-157.
    PUB | DOI
     
  • [29]
    2001 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993768
    B. Hammer and T. Villmann, “Input Pruning for Neural Gas Architectures”, Proc. Of European Symposium on Artificial Neural Networks (ESANN'2001), Brussels, Belgium: D facto publications, 2001, pp.283-288.
    PUB
     
  • [28]
    2001 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993343
    T. Bojer, et al., “Relevance determination in learning vector quantization”, ESANN'2001, M. Verleysen, ed., D-facto publications, 2001, pp.271-276.
    PUB
     
  • [27]
    2001 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994123
    M. Vidyasagar, S. Balaji, and B. Hammer, “Closure properties of uniform convergence of empirical means and PAC learnability under a family of probability measures”, System and Control Letters, vol. 42, 2001, pp. 151-157.
    PUB
     
  • [26]
    2001 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993474
    B. Hammer, “On the Generalization Ability of Recurrent Networks”, Artificial Neural Networks. Proceedings. Lecture Notes in Computer Science, 2130, G. Dorffner, H. Bischof, and K. Hornik, eds., Berlin: Springer, 2001, pp.731-736.
    PUB
     
  • [25]
    2001 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993739
    B. Hammer and T. Villmann, “Estimating Relevant Input Dimensions for Self-Organizing Algorithms”, Advances in Self-Organising Maps, N.M. Allinson, et al., eds., London: Springer, 2001, pp.173-180.
    PUB
     
  • [24]
    2001 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994042
    M. Strickert, T. Bojer, and B. Hammer, “Generalized Relevance LVQ for Time Series”, Artificial Neural Networks. International Conference. Proceedings. Lecture Notes in Computer Science, 2130, G. Dorffner, H. Bischof, and K. Hornik, eds., Berlin: Springer, 2001, pp.677-683.
    PUB
     
  • [23]
    2001 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993510
    B. Hammer, “Generalization Ability of Folding Networks.”, IEEE Trans. Knowl. Data Eng., vol. 13, 2001, pp. 196-206.
    PUB
     
  • [22]
    2000 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2982131
    B. Hammer, “On the approximation capability of recurrent neural networks”, Neurocomputing, vol. 31, 2000, pp. 107-123.
    PUB | DOI
     
  • [21]
    2000 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993499
    B. Hammer, “Limitations of hybrid systems”, European Symposium on Artificial Neural Networks, M. Verleysen, ed., D-facto publications, 2000, pp.213-218.
    PUB
     
  • [20]
    2000 | Monographie | Veröffentlicht | PUB-ID: 1993514
    B. Hammer, Learning with Recurrent Neural Networks, Lecture Notes in Control and Information Sciences, 254, Berlin: Springer, 2000.
    PUB
     
  • [19]
    2000 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993512
    B. Hammer, “On the approximation capability of recurrent neural networks”, Neurocomputing, vol. 31, 2000, pp. 107-123.
    PUB
     
  • [18]
    2000 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993400
    B. DasGupta and B. Hammer, “On Approximate Learning by Multi-layered Feedforward Circuits.”, Algorithmic Learning Theory, 11th International Conference. Proceedings. Lecture Notes in Computer Science, 1968, H. Arimura, S. Jain, and A. Sharma, eds., Berlin: Springer, 2000, pp.264-278.
    PUB
     
  • [17]
    2000 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993479
    B. Hammer, “Approximation and generalization issues of recurrent networks dealing with structured data”, ECAI workshop: Foundations of connectionist-symbolic integration: representation, paradigms, and algorithms, P. Frasconi, A. Sperduti, and M. Gori, eds., 2000.
    PUB
     
  • [16]
    2000 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993495
    B. Hammer, “Neural networks classifying symbolic data”, ICML workshop on attribute-value and relational learning: crossing the boundaries, L. de Raedt and S. Kramer, eds., 2000, pp.61-65.
    PUB
     
  • [15]
    1999 | Dissertation | PUB-ID: 2999889 OA
    B. Hammer, Learning with Recurrent Neural Networks (PhD thesis), Universität Osnabrück: 1999.
    PUB | PDF
     
  • [14]
    1999 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2982132
    B. Hammer, “On the Learnability of Recursive Data”, Mathematics of Control, Signals, and Systems, vol. 12, 1999, pp. 62-79.
    PUB | DOI
     
  • [13]
    1999 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993516
    B. Hammer, “On the learnability of recursive data”, Mathematics of Control, Signals and Systems, vol. 12, 1999, pp. 62-79.
    PUB
     
  • [12]
    1999 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993502
    B. Hammer, “Approximation capabilities of folding networks”, European Symposium on Artificial Neural Networks, M. Verleysen, ed., D-facto publications, 1999, pp.33-38.
    PUB
     
  • [11]
    1999 | Report | Veröffentlicht | PUB-ID: 1993409
    B. DasGupta and B. Hammer, Hardness of approximation of the loading problem for multi-layered feedforward neural networks, DIMACS Center, Rutgers University, 1999.
    PUB
     
  • [10]
    1998 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993484
    B. Hammer, “On the Approximation Capability of Recurrent Neural Networks”, Proceedings of the International ICSC / IFAC Symposium on Neural Computation (NC 1998), M. Heiss, ed., ICSC Academic Press, 1998, pp.512-518.
    PUB
     
  • [9]
    1998 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993505
    B. Hammer, “Training a sigmoidal network is difficult”, European Symposium on Artificial Neural Networks, M. Verleysen, ed., D-facto publications, 1998, pp.255-260.
    PUB
     
  • [8]
    1998 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993518
    B. Hammer, “Some complexity results for perceptron networks”, International Conference on artificial Neural Networks, 1998, pp.639-644.
    PUB
     
  • [7]
    1997 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993526
    B. Hammer, “Generalization of Elman Networks”, Artificial Neural Networks - ICANN '97, 7th International Conference. Proceedings. Lecture Notes in Computer Science, 1327, Berlin: Springer, 1997, pp.409-414.
    PUB
     
  • [6]
    1997 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993684
    B. Hammer and V. Sperschneider, “Neural networks can approximate mappings on structured objects”, International conference on Computational Intelligence and Neural Networks, P.P. Wang, ed., 1997, pp.211-214.
    PUB
     
  • [5]
    1997 | Report | Veröffentlicht | PUB-ID: 1993524
    B. Hammer, On the generalization ability of simple recurrent neural networks, Osnabrücker Schriften zur Mathematik, Osnabrück: Universität Osnabrück, 1997.
    PUB
     
  • [4]
    1997 | Report | Veröffentlicht | PUB-ID: 1993520
    B. Hammer, Learning recursive data is intractable, Osnabrücker Schriften zur Mathematik, Osnabrück: Universität Osnabrück, 1997.
    PUB
     
  • [3]
    1997 | Report | Veröffentlicht | PUB-ID: 1993522
    B. Hammer, A NP-hardness result for a sigmoidal 3-node neural network, Osnabrücker Schriften zur Mathematik, Osnabrück: Universität Osnabrück, 1997.
    PUB
     
  • [2]
    1996 | Report | Veröffentlicht | PUB-ID: 1993528
    B. Hammer, Universal approximation of mappings on structured objects using the folding architecture, Osnabrücker Schriften zur Mathematik, Osnabrück: Universität Osnabrück, 1996.
    PUB
     
  • [1]
    1996 | Monographie | Veröffentlicht | PUB-ID: 1994039
    V. Sperschneider and B. Hammer, Theoretische Informatik. Eine problemorientierte Einführung, erlin: Springer, 1996.
    PUB
     
Markierte Publikation(en)

Suche

Publikationen filtern

Darstellung / Sortierung

Export / Einbettung