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
    Feldhans, R., & Hammer, B., 2025. Towards Reliable Drift Detection and Explanation in Text Data. In Intelligent Data Engineering and Automated Learning – IDEAL 2024, PT I. Lecture Notes in Computer Science. no.15346 Cham: Springer , pp. 301-312.
    PUB | DOI | WoS
     
  • [550]
    2025 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 3001658
    Peters, H., et al., 2025. Development of a digital twin for data-driven modeling of punch-bending processes using a graphical modeling notation. at - Automatisierungstechnik, 73(3), p 173-184.
    PUB | DOI | WoS
     
  • [549]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2989164
    Velioglu, R., Chan, R.K.-W., & Hammer, B., 2024. FashionFail: Addressing Failure Cases in Fashion Object Detection and Segmentation. In 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).
    PUB | DOI | WoS | arXiv
     
  • [548]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 3001615
    Markmann, T., Straat, M., & Hammer, B., 2024. Koopman-Based Surrogate Modelling of Turbulent Rayleigh-Benard Convection. In 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).
    PUB | DOI | WoS
     
  • [547]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 3001626
    Internò, C., et al., 2024. Federated Loss Exploration for Improved Convergence on Non-IID Data. In 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).
    PUB | DOI | WoS
     
  • [546]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2993740
    Vaquet, V., et al., 2024. Localizing of Anomalies in Critical Infrastructure using Model-Based Drift Explanations. In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 1-8.
    PUB | DOI | WoS
     
  • [545]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2992441
    Schroeder, S., Schulz, A., & Hammer, B., 2024. The SAME score: Improved cosine based measure for semantic bias. In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 1-8.
    PUB | DOI | WoS
     
  • [544]
    2024 | Konferenzbeitrag | PUB-ID: 3000176
    Fumagalli, F., et al., 2024. KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions. In R. Salakhutdinov, et al., eds. Proceedings of the 41st International Conference on Machine Learning. Proceedings of Machine Learning Research. no.235 PMLR, pp. 14308-14342.
    PUB
     
  • [543]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2993714 OA
    Mazur, A., et al., 2024. Visualizing and Improving 3D Mesh Segmentation with DeepView. In ESANN 2024 proceedings. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com, pp. 649-654.
    PUB | PDF | DOI | Download (ext.)
     
  • [542]
    2024 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2993739
    Vaquet, V., et al., 2024. Challenges, Methods, Data–A Survey of Machine Learning in Water Distribution Networks. In M. Wand, et al., eds. Artificial Neural Networks and Machine Learning – ICANN 2024. 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17–20, 2024, Proceedings, Part IX. Lecture Notes in Computer Science. Cham: Springer Nature Switzerland, pp. 155-170.
    PUB | DOI | WoS
     
  • [541]
    2024 | Preprint | PUB-ID: 2999454
    Velioglu, R., et al., 2024. TryOffDiff: Virtual-Try-Off via High-Fidelity Garment Reconstruction using Diffusion Models. arXiv:2411.18350.
    PUB | arXiv
     
  • [540]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2994141
    Vieth, M., et al., 2024. Similarity-Based Zero-Shot Domain Adaptation for Wearables. In ESANN 2024 proceedings. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com, pp. 221-226.
    PUB | DOI
     
  • [539]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2994158
    Hammer, B., 2024. Explaining Neural Networks - Deep and Shallow. In T. Villmann, et al., eds. Advances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and Beyond (WSOM+ 2024) . Lecture Notes in Networks and Systems. no.1087 Cham: Springer , pp. 139-140.
    PUB | DOI | WoS
     
  • [538]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2991394
    Kolpaczki, P., et al., 2024. SVARM-IQ: Efficient Approximation of Any-order Shapley Interactions through Stratification. In S. Dasgupta, S. Mandt, & Y. Li, eds. International Conference on Artificial Intelligence and Statistics, 2-4 May 2024, Palau de Congressos, Valencia, Spain. Proceedings of Machine Learning Research. no.238 San Diego: PMLR, pp. 3520-3528.
    PUB | WoS | arXiv
     
  • [537]
    2024 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2994227
    Artelt, A., et al., 2024. EPyT-Flow: A Toolkit for Generating Water DistributionNetwork Data. Journal of Open Source Software, 9(103): 7104.
    PUB | DOI
     
  • [536]
    2024 | Konferenzbeitrag | PUB-ID: 2992095
    Störck, F., et al., 2024. FairGLVQ: Fairness in Partition-Based Classification. In T. Villmann, et al., eds. Proceedings of the 15th International Workshop on Self-Organizing Maps (WSOM 2024). Cham: Springer Nature Switzerland, pp. 141-151.
    PUB | DOI | Download (ext.) | WoS
     
  • [535]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2993721
    Peters, H., et al., 2024. Novel approach for data-driven modelling of multi-stage straightening and bending processes. In Material Forming: ESAFORM 2024. Materials Research Proceedings. no.41 Materials Research Forum LLC, pp. 2289-2298.
    PUB | DOI
     
  • [534]
    2024 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2993652
    Shah, Z.H., et al., 2024. Image restoration in frequency space using complex-valued CNNs. Frontiers in Artificial Intelligence , 7: 1353873.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [533]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2993852
    Liuliakov, A., et al., 2024. Noise Robust One-Class Intrusion Detection on Dynamic Graphs. In ESANN 2024 proceedings. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com, pp. 363-368.
    PUB | DOI
     
  • [532]
    2024 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2991468 OA
    Hinder, F., Vaquet, V., & Hammer, B., 2024. 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, 7.
    PUB | PDF | DOI | WoS | PubMed | Europe PMC
     
  • [531]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2993744
    Vaquet, V., et al., 2024. Self-Supervised Learning from Incrementally Drifting Data Streams. In ESANN 2024 proceesdings. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com, pp. 431-436.
    PUB | DOI
     
  • [530]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2993743
    Hinder, F., Vaquet, V., & Hammer, B., 2024. On the Fine Structure of Drifting Features. In ESANN 2024 proceesdings. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com, pp. 63-68.
    PUB | DOI
     
  • [529]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2993741
    Brinkrolf, J., et al., 2024. Causes of Rejects in Prototype-based Classification Aleatoric vs. Epistemic Uncertainty. In ESANN 2024 proceesdings. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com, pp. 191-196.
    PUB | DOI
     
  • [528]
    2024 | Report | Veröffentlicht | PUB-ID: 2992602 OA
    Hammer, B., et al., 2024. Sustainable Life-Cycle of Intelligent Socio-Technical Systems,
    PUB | Dateien verfügbar | DOI
     
  • [527]
    2024 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2988509
    Hinder, F., Vaquet, V., & Hammer, B., 2024. A Remark on Concept Drift for Dependent Data. In I. Miliou, N. Piatkowski, & P. Papapetrou, eds. Advances in Intelligent Data Analysis XXII. 22nd International Symposium on Intelligent Data Analysis, IDA 2024, Stockholm, Sweden, April 24–26, 2024, Proceedings, Part I. Lecture Notes in Computer Science. Cham: Springer Nature Switzerland, pp. 77-89.
    PUB | DOI | WoS
     
  • [526]
    2024 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2992700
    Strotherm, J., et al., 2024. Fairness in KI-Systemen. In S. Schork, ed. Vertrauen in Künstliche Intelligenz. Eine multi-perspektivische Betrachtung. Wiesbaden: Springer Fachmedien Wiesbaden, pp. 163-183.
    PUB | DOI | arXiv
     
  • [525]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2992337 OA
    Zanutto, D., et al., 2024. A Water Futures Approach on Water Demand Forecasting with Online Ensemble Learning. In The 3rd International Joint Conference on Water Distribution Systems Analysis & Computing and Control for the Water Industry (WDSA/CCWI 2024). Basel Switzerland: MDPI, pp. 60.
    PUB | PDF | DOI
     
  • [524]
    2024 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2991444
    Hinder, F., Vaquet, V., & Hammer, B., 2024. Feature-based analyses of concept drift. Neurocomputing, 600: 127968.
    PUB | DOI | WoS
     
  • [523]
    2024 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2991310 OA
    Hinder, F., Vaquet, V., & Hammer, B., 2024. 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 , 7: 1330257.
    PUB | PDF | DOI | WoS | PubMed | Europe PMC
     
  • [522]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2987572
    Schroeder, S., et al., 2024. Semantic Properties of Cosine Based Bias Scores for Word Embeddings. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods. Vol. 1. Setúbal, Portugal: SCITEPRESS - Science and Technology Publications, pp. 160-168.
    PUB | DOI
     
  • [521]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2987573
    Grimmelsmann, N., et al., 2024. 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. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies. Setúbal, Portugal: SCITEPRESS - Science and Technology Publications, pp. 611-621.
    PUB | DOI
     
  • [520]
    2024 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2988165
    Muschalik, M., et al., 2024. Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles. Proceedings of the AAAI Conference on Artificial Intelligence, 38(13), p 14388-14396.
    PUB | DOI
     
  • [519]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2988098
    Vaquet, V., Hinder, F., & Hammer, B., 2024. Investigating the Suitability of Concept Drift Detection for Detecting Leakages in Water Distribution Networks. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods ICPRAM. Volume 1. SCITEPRESS - Science and Technology Publications, pp. 296-303.
    PUB | DOI
     
  • [518]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2984049
    Ashraf, M.I., et al., 2023. Spatial Graph Convolution Neural Networks for Water Distribution Systems. In B. Crémilleux, S. Hess, & S. Nijssen, eds. Advances in Intelligent Data Analysis XXI. 21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-la-Neuve, Belgium, April 12–14, 2023, Proceedings. Lecture Notes in Computer Science. Cham: Springer Nature Switzerland, pp. 29-41.
    PUB | DOI
     
  • [517]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2983942
    Muschalik, M., et al., 2023. iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios. In L. Longo, ed. Explainable Artificial Intelligence. First World Conference, xAI 2023, Lisbon, Portugal, July 26–28, 2023, Proceedings, Part I. Communications in Computer and Information Science. Cham: Springer Nature Switzerland, pp. 177-194.
    PUB | DOI | WoS
     
  • [516]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2983795
    Kuhl, U., Artelt, A., & Hammer, B., 2023. For Better or Worse: The Impact of Counterfactual Explanations’ Directionality on User Behavior in xAI. In L. Longo, ed. Explainable Artificial Intelligence. First World Conference, xAI 2023, Lisbon, Portugal, July 26–28, 2023, Proceedings, Part III. Communications in Computer and Information Science. Cham: Springer Nature Switzerland, pp. 280-300.
    PUB | DOI | WoS
     
  • [515]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2987580
    Fumagalli, F., et al., 2023. SHAP-IQ: Unified Approximation of any-order Shapley Interactions. In Advances in Neural Information Processing Systems 36 (NeurIPS 2023). Advances in Neural Information Processing Systems.
    PUB | Download (ext.) | WoS | arXiv
     
  • [514]
    2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2980429 OA
    Kummert, J., Schulz, A., & Hammer, B., 2023. Metric Learning with Self-Adjusting Memory for Explaining Feature Drift. SN Computer Science, 4(4): 376.
    PUB | PDF | DOI
     
  • [513]
    2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2979703
    Liuliakov, A., Hermes, L., & Hammer, B., 2023. AutoML technologies for the identification of sparse classification and outlier detection models. Applied Soft Computing, 133: 109942.
    PUB | DOI | WoS
     
  • [512]
    2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2979026
    Jakob, J., et al., 2023. Interpretable SAM-kNN Regressor for Incremental Learning on High-Dimensional Data Streams. Applied Artificial Intelligence, 37(1): 2198846.
    PUB | DOI | WoS
     
  • [511]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2985684
    Kummert, J., et al., 2023. Generating Cardiovascular Data to Improve Training of Assistive Heart Devices. In 2023 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, pp. 1292-1297.
    PUB | DOI
     
  • [510]
    2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2969734 OA
    Kuhl, U., Artelt, A., & Hammer, B., 2023. Let's go to the Alien Zoo: Introducing an experimental framework to study usability of counterfactual explanations for machine learning. Frontiers in Computer Science, 5: 1087929.
    PUB | PDF | DOI | Download (ext.) | WoS | arXiv
     
  • [509]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2985683
    Feldhans, R., et al., 2023. Data Augmentation for Cardiovascular Time Series Data Using WaveNet. In 2023 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, pp. 836-841.
    PUB | DOI
     
  • [508]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2980971
    Strotherm, J., & Hammer, B., 2023. Fairness-Enhancing Ensemble Classification in Water Distribution Networks. Presented at the International Work-Conference on Artificial Neural Networks (IWANN) 2023, Ponta Delgada.
    PUB | DOI | Download (ext.)
     
  • [507]
    2023 | Konferenzbeitrag | Angenommen | PUB-ID: 2982899 OA
    Vaquet, V., Brinkrolf, J., & Hammer, B., Accepted. Robust Feature Selection and Robust Training to Cope with Hyperspectral Sensor Shifts.
    PUB | PDF
     
  • [506]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2977934
    Hinder, F., et al., 2023. On the Change of Decision Boundary and Loss in Learning with Concept Drift. In B. Crémilleux, S. Hess, & S. Nijssen, eds. Advances in Intelligent Data Analysis XXI. 21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-la-Neuve, Belgium, April 12–14, 2023, Proceedings. Lecture Notes in Computer Science. no.13876 Cham: Springer , pp. 182-194.
    PUB | DOI
     
  • [505]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982167
    Hinder, F., et al., 2023. On the Hardness and Necessity of Supervised Concept Drift Detection. In M. De Marsico, G. Sanniti di Baja, & A. Fred, eds. Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods ICPRAM. Vol. 1. Setúbal: SCITEPRESS - Science and Technology Publications, pp. 164-175.
    PUB | DOI
     
  • [504]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969381
    Schroeder, S., et al., 2023. So Can We Use Intrinsic Bias Measures or Not? In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods. Setúbal, Portugal: SCITEPRESS - Science and Technology Publications, pp. 403-410.
    PUB | DOI
     
  • [503]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969382
    Kenneweg, P., et al., 2023. Debiasing Sentence Embedders Through Contrastive Word Pairs. In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods. Setúbal, Portugal: SCITEPRESS - Science and Technology Publications, pp. 205-212.
    PUB | DOI
     
  • [502]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969383
    Artelt, A., Schulz, A., & Hammer, B., 2023. "Why Here and not There?": Diverse Contrasting Explanations of Dimensionality Reduction. In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods. Setúbal, Portugal: SCITEPRESS - Science and Technology Publications, pp. 27-38.
    PUB | DOI | arXiv
     
  • [501]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2983756
    Fumagalli, F., et al., 2023. On Feature Removal for Explainability in Dynamic Environments. In ESANN 2023 proceedings. pp. 83-88.
    PUB | DOI
     
  • [500]
    2023 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2983727
    Fumagalli, F., et al., 2023. Incremental permutation feature importance (iPFI): towards online explanations on data streams. Machine Learning .
    PUB | DOI | WoS
     
  • [499]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2983250
    Vieth, M., Schulz, A., & Hammer, B., 2023. Extending Drift Detection Methods to Identify When Exactly the Change Happened. In I. Rojas, G. Joya, & A. Catala, eds. Advances in Computational Intelligence. 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Ponta Delgada, Portugal, June 19–21, 2023, Proceedings, Part I. Lecture Notes in Computer Science. Cham: Springer Nature Switzerland, pp. 92-104.
    PUB | DOI
     
  • [498]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2983455
    Liuliakov, A., et al., 2023. One-Class Intrusion Detection with Dynamic Graphs. In L. Iliadis, et al., eds. 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. Lecture Notes in Computer Science. Cham: Springer Nature Switzerland, pp. 537-549.
    PUB | DOI
     
  • [497]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2983943
    Muschalik, M., et al., 2023. iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams. In D. Koutra, et al., eds. Machine Learning and Knowledge Discovery in Databases: Research Track. European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part III. Lecture Notes in Computer Science. Cham: Springer Nature Switzerland, pp. 428-445.
    PUB | DOI
     
  • [496]
    2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2981289
    Hinder, F., et al., 2023. Model-based explanations of concept drift. Neurocomputing, : 126640.
    PUB | DOI | Download (ext.) | WoS
     
  • [495]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2985571
    Artelt, A., et al., 2023. Unsupervised Unlearning of Concept Drift with Autoencoders. In 2023 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, pp. 703-710.
    PUB | DOI
     
  • [494]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982830
    Hinder, F., & Hammer, B., 2023. Feature Selection for Concept Drift Detection. In M. Verleysen, ed. ESANN 2023 Proceedings.
    PUB
     
  • [493]
    2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2983759
    Koundouri, P., et al., 2023. Behavioral Economics and Neuroeconomics of Environmental Values. Annual Review of Resource Economics, 15(1), p 153-176.
    PUB | DOI | Download (ext.) | WoS
     
  • [492]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2984048
    Schulte-Schüren, C., et al., 2023. Best of both, Structured and Unstructured Sparsity in Neural Networks. In Proceedings of the 3rd Workshop on Machine Learning and Systems. New York, NY, USA: ACM, pp. 104-108.
    PUB | DOI
     
  • [491]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2984047
    Kenneweg, P., et al., 2023. Faster Convergence for Transformer Fine-tuning with Line Search Methods. In 2023 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 1-8.
    PUB | DOI
     
  • [490]
    2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2983728
    Artelt, A., Visser, R., & Hammer, B., 2023. "I do not know! but why?"- Local model-agnostic example-based explanations of reject. Neurocomputing, 558: 126722.
    PUB | DOI | WoS
     
  • [489]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2983457
    Schroeder, S., et al., 2023. Measuring Fairness with Biased Data: A Case Study on the Effects of Unsupervised Data in Fairness Evaluation. In I. Rojas, G. Joya, & A. Catala, eds. Advances in Computational Intelligence. 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Ponta Delgada, Portugal, June 19–21, 2023, Proceedings, Part I. Lecture Notes in Computer Science. Cham: Springer Nature Switzerland, pp. 134-145.
    PUB | DOI
     
  • [488]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2983406
    Stahlhofen, P., et al., 2023. Adversarial Attacks on Leakage Detectors in Water Distribution Networks. In I. Rojas, G. Joya, & A. Catala, eds. Advances in Computational Intelligence. 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Ponta Delgada, Portugal, June 19–21, 2023, Proceedings, Part II. Lecture Notes in Computer Science. Cham: Springer Nature Switzerland, pp. 451-463.
    PUB | DOI | Preprint
     
  • [487]
    2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2978162 OA
    Stallmann, D., & Hammer, B., 2023. Unsupervised Cyclic Siamese Networks Automating Cell Imagery Analysis. Algorithms, 16(4): 205.
    PUB | PDF | DOI | WoS
     
  • [486]
    2023 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2968921
    Schilling, M., et al., 2023. Modularity in Nervous Systems-a Key to Efficient Adaptivity for Deep Reinforcement Learning. Cognitive Computation.
    PUB | DOI | WoS
     
  • [485]
    2022 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2962746 OA
    Artelt, A., et al., 2022. Contrasting Explanations for Understanding and Regularizing Model Adaptations. Neural Processing Letters, 55, p 5273–5297.
    PUB | PDF | DOI | Download (ext.) | WoS
     
  • [484]
    2022 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2967683 OA
    Kenneweg, P., Stallmann, D., & Hammer, B., 2022. Novel transfer learning schemes based on Siamese networks and synthetic data. Neural Computing and Applications, 35, p 8423–8436.
    PUB | PDF | DOI | Download (ext.) | WoS | PubMed | Europe PMC
     
  • [483]
    2022 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982135
    Jakob, J., Hasenjäger, M., & Hammer, B., 2022. Reject Options for Incremental Regression Scenarios. In E. Pimenidis, et al., eds. Artificial Neural Networks and Machine Learning – ICANN 2022. 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings; Part IV. Lecture Notes in Computer Science. Cham: Springer Nature Switzerland, pp. 248-259.
    PUB | DOI
     
  • [482]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969736
    Kuhl, U., Artelt, A., & Hammer, B., 2022. Keep Your Friends Close and Your Counterfactuals Closer: Improved Learning From Closest Rather Than Plausible Counterfactual Explanations in an Abstract Setting. In 2022 ACM Conference on Fairness, Accountability, and Transparency. New York, NY, USA: ACM, pp. 2125-2137.
    PUB | DOI | Download (ext.)
     
  • [481]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969235
    Castellani, A., Schmitt, S., & Hammer, B., 2022. Stream-Based Active Learning with Verification Latency in Non-stationary Environments. In E. Pimenidis, et al., eds. Artificial Neural Networks and Machine Learning – ICANN 2022. 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings; Part IV. Lecture Notes in Computer Science. no.13532 Cham: Springer Nature Switzerland, pp. 260-272.
    PUB | DOI | Download (ext.)
     
  • [480]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969461
    Artelt, A., & Hammer, B., 2022. “Even if …” – Diverse Semifactual Explanations of Reject. In H. Ishibuchi, ed. 2022 IEEE Symposium Series on Computational Intelligence (SSCI). Piscataway, NJ: IEEE, pp. 854-859.
    PUB | DOI
     
  • [479]
    2022 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2967296
    Velioglu, R., et al., 2022. Explainable Artificial Intelligence for Improved Modeling of Processes. In H. Yin, D. Camacho, & P. Tino, eds. Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings. Lecture Notes in Computer Science. no.13756 Cham: Springer International Publishing, pp. 313-325.
    PUB | DOI
     
  • [478]
    2022 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2969459
    Jakob, J., et al., 2022. SAM-kNN Regressor for Online Learning in Water Distribution Networks. In E. Pimenidis, et al., eds. Artificial Neural Networks and Machine Learning – ICANN 2022. 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings, Part III. Lecture Notes in Computer Science. no.13531 Cham: Springer Nature , pp. 752-762.
    PUB | DOI
     
  • [477]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969460
    Artelt, A., et al., 2022. Explaining Reject Options of Learning Vector Quantization Classifiers. In Proceedings of the 14th International Joint Conference on Computational Intelligence. SCITEPRESS - Science and Technology Publications, pp. 249-261.
    PUB | DOI
     
  • [476]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2966088
    Hinder, F., et al., 2022. Localization of Concept Drift: Identifying the Drifting Datapoints. In 2022 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 1-9.
    PUB | DOI | Download (ext.)
     
  • [475]
    2022 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2967410
    Vieth, M., et al., 2022. Efficient Sensor Selection for Individualized Prediction Based on Biosignals. In H. Yin, D. Camacho, & P. Tino, eds. Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings. Lecture Notes in Computer Science. no.13756 Cham: Springer International Publishing, pp. 326-337.
    PUB | DOI | Download (ext.)
     
  • [474]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2966600
    Kenneweg, P., Schroeder, S., & Hammer, B., 2022. Neural Architecture Search for Sentence Classification with BERT. In ESANN 2022 proceedings. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com, pp. 417-422.
    PUB | DOI
     
  • [473]
    2022 | Konferenzbeitrag | Angenommen | PUB-ID: 2964534
    Vaquet, V., et al., Accepted. 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.
    PUB
     
  • [472]
    2022 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2964421
    Muschalik, M., et al., 2022. Agnostic Explanation of Model Change based on Feature Importance. KI - Künstliche Intelligenz.
    PUB | DOI | Download (ext.) | WoS
     
  • [471]
    2022 | Report | Veröffentlicht | PUB-ID: 2965286
    Artelt, A., et al., 2022. Faire Algorithmen und die Fairness von Erklärungen: Informatische, rechtliche und ethische Perspektiven, DuEPublico: Duisburg-Essen Publications online, University of Duisburg-Essen, Germany.
    PUB | DOI | Download (ext.)
     
  • [470]
    2022 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2962928
    Vaquet, V., et al., 2022. Investigating Intensity and Transversal Drift in Hyperspectral Imaging Data. Neurocomputing.
    PUB | DOI | WoS
     
  • [469]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2962650 OA
    Vaquet, V., et al., 2022. 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.
    PUB | PDF
     
  • [468]
    2022 | Kurzbeitrag Konferenz / Poster | PUB-ID: 2962861
    Hinder, F., et al., 2022. Localization of Concept Drift: Identifying the Drifting Datapoints.
    PUB
     
  • [467]
    2022 | Preprint | PUB-ID: 2962919 OA
    Artelt, A., et al., 2022. One Explanation to Rule them All — Ensemble Consistent Explanations. ArXiv:2205.08974 .
    PUB | PDF | Download (ext.) | arXiv
     
  • [466]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2967096
    Kenneweg, P., et al., 2022. Intelligent Learning Rate Distribution to Reduce Catastrophic Forgetting in Transformers. In H. Yin, D. Camacho, & P. Tino, eds. Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings. Lecture Notes in Computer Science. no.13756 Cham: Springer International Publishing, pp. 252-261.
    PUB | DOI
     
  • [465]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2987492
    Savic, D., et al., 2022. Long-Term Transitioning of Water Distribution Systems: ERC Water-Futures Project. In 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.
    PUB | DOI
     
  • [464]
    2022 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2984050
    Hinder, F., Vaquet, V., & Hammer, B., 2022. Suitability of Different Metric Choices for Concept Drift Detection. In T. Bouadi, E. Fromont, & E. Hüllermeier, eds. Advances in Intelligent Data Analysis XX. 20th International Symposium on Intelligent Data Analysis, IDA 2022, Rennes, France, April 20–22, 2022, Proceedings. Lecture Notes in Computer Science. Cham: Springer International Publishing, pp. 157-170.
    PUB | DOI
     
  • [463]
    2022 | Zeitschriftenaufsatz | PUB-ID: 2978998
    Paaßen, B., et al., 2022. Reservoir Memory Machines as Neural Computers. IEEE Transactions on Neural Networks and Learning Systems, 33(6), p 2575–2585.
    PUB | DOI | Download (ext.) | arXiv
     
  • [462]
    2022 | Report | Veröffentlicht | PUB-ID: 2965622 OA
    Hammer, B., et al., 2022. 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.
    PUB | PDF | DOI
     
  • [461]
    2022 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2964829
    Langnickel, L., et al., 2022. BERT WEAVER: Using WEight AVERaging to Enable Lifelong Learning for Transformer-based Models. arXiv.
    PUB | DOI | arXiv
     
  • [460]
    2022 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2961873
    Göpfert, J.P., Wersing, H., & Hammer, B., 2022. Interpretable locally adaptive nearest neighbors. Neurocomputing, 470, p 344-351.
    PUB | DOI | WoS
     
  • [459]
    2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2958662
    Schilling, M., et al., 2021. Decentralized control and local information for robust and adaptive decentralized Deep Reinforcement Learning. Neural Networks, 144, p 699-725.
    PUB | DOI | Download (ext.) | WoS | PubMed | Europe PMC
     
  • [458]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982134
    Castellani, A., Schmitt, S., & Hammer, B., 2021. Task-Sensitive Concept Drift Detector with Constraint Embedding. In 2021 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, pp. 01-08.
    PUB | DOI
     
  • [457]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982136
    Jakob, J., Hasenjäger, M., & Hammer, B., 2021. On the suitability of incremental learning for regression tasks in exoskeleton control. In 2021 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, pp. 1-8.
    PUB | DOI
     
  • [456]
    2021 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982165
    Liuliakov, A., & Hammer, B., 2021. AutoML Technologies for the Identification of Sparse Models. In H. Yin, et al., eds. Intelligent Data Engineering and Automated Learning – IDEAL 2021. 22nd International Conference, IDEAL 2021, Manchester, UK, November 25–27, 2021, Proceedings. Lecture Notes in Computer Science. no.13113 Cham: Springer , pp. 65-75.
    PUB | DOI
     
  • [455]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969237
    Castellani, A., Schmitt, S., & Hammer, B., 2021. Estimating the Electrical Power Output of Industrial Devices with End-to-End Time-Series Classification in the Presence of Label Noise. In N. Oliver, et al., eds. Machine Learning and Knowledge Discovery in Databases. Research Track. European Conference, ECML PKDD 2021, Bilbao, Spain, September 13–17, 2021, Proceedings, Part I. Lecture Notes in Computer Science. no.12975 Cham: Springer International Publishing, pp. 469-484.
    PUB | DOI | Download (ext.)
     
  • [454]
    2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2957340
    Artelt, A., & Hammer, B., 2021. Efficient computation of counterfactual explanations and counterfactual metrics of prototype-based classifiers. Neurocomputing, 470(VSI: ESANN 2020), p 304-317.
    PUB | DOI | Download (ext.) | WoS
     
  • [453]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2957373
    Artelt, A., et al., 2021. Contrastive Explanations for Explaining Model Adaptations. In I. Rojas, G. Joya, & A. Catala, eds. Advances in Computational Intelligence. 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Virtual Event, June 16–18, 2021, Proceedings, Part I. Lecture Notes in Computer Science. Cham: Springer , pp. 101-112.
    PUB | DOI
     
  • [452]
    2021 | Preprint | PUB-ID: 2959899
    Artelt, A., & Hammer, B., 2021. Convex optimization for actionable & plausible counterfactual explanations. arXiv: 2105.07630v1.
    PUB | Download (ext.) | arXiv
     
  • [451]
    2021 | Konferenzbeitrag | PUB-ID: 2959428
    Hinder, F., et al., 2021. Fast Non-Parametric Conditional Density Estimation using Moment Trees. IEEE Computational Intelligence Magazine.
    PUB
     
  • [450]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2960754
    Hinder, F., et al., 2021. A Shape-Based Method for Concept Drift Detection and Signal Denoising. In 2021 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings. Piscataway, NJ: IEEE, pp. 01-08.
    PUB | DOI
     
  • [449]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2960755
    Hinder, F., et al., 2021. Fast Non-Parametric Conditional Density Estimation using Moment Trees. In 2021 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings. Piscataway, NJ: IEEE, pp. 1-7.
    PUB | DOI
     
  • [448]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2960685
    Vaquet, V., et al., 2021. Investigating Intensity and Transversal Drift in Hyperspectral Imaging Data. In M. Verleysen, ed. ESANN 2021 proceedings. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com, pp. 47-52.
    PUB | DOI
     
  • [447]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2960687
    Vaquet, V., et al., 2021. Online Learning on Non-Stationary Data Streams for Image Recognition using Deep Embeddings. IEEE Symposium Series on Computational Intelligence, , p 1-7.
    PUB | DOI
     
  • [446]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2962747
    Artelt, A., et al., 2021. Evaluating Robustness of Counterfactual Explanations. In 2021 IEEE Symposium Series on Computational Intelligence (SSCI). Piscataway, NJ: IEEE, pp. 01-09.
    PUB | DOI
     
  • [445]
    2021 | Report | Veröffentlicht | PUB-ID: 2954239
    Szczuka, J., et al., 2021. Können Kinder aufgeklärte Nutzer* innen von Sprachassistenten sein? Rechtliche, psychologische, ethische und informatische Perspektiven, Essen: Universität Duisburg-Essen, Universitätsbibliothek.
    PUB | DOI
     
  • [444]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2957588
    Artelt, A., & Hammer, B., 2021. Efficient computation of contrastive explanations. In 2021 International Joint Conference on Neural Networks (IJCNN). New York: Institute of Electrical and Electronics Engineers (IEEE), pp. 1-9.
    PUB | DOI | Download (ext.)
     
  • [443]
    2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2949334 OA
    Rohlfing, K., et al., 2021. Explanation as a social practice: Toward a conceptual framework for the social design of AI systems. IEEE Transactions on Cognitive and Developmental Systems, 13(3), p 717--728.
    PUB | PDF | DOI | WoS
     
  • [442]
    2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2954542
    Paaßen, B., Schulz, A., & Hammer, B., 2021. Reservoir Stack Machines. Neurocomputing, 470, p 352-364.
    PUB | DOI | Download (ext.) | WoS | arXiv
     
  • [441]
    2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2959418
    Göpfert, J.P., et al., 2021. Intuitiveness in Active Teaching. IEEE Transactions on Human-Machine Systems, , p 1-10.
    PUB | DOI | WoS
     
  • [440]
    2021 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2956229
    Paassen, B., et al., 2021. Reservoir Memory Machines as Neural Computers. IEEE Transactions on Neural Networks and Learning Systems, , p 1-11.
    PUB | DOI | Download (ext.) | WoS | PubMed | Europe PMC | arXiv
     
  • [439]
    2021 | Zeitschriftenaufsatz | Angenommen | PUB-ID: 2955245
    Stallmann, D., et al., Accepted. Towards an automatic analysis of CHO-K1 suspension growth in microfluidic single-cell cultivation. Bioinformatics .
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [438]
    2021 | Konferenzbeitrag | PUB-ID: 2958664
    Hermes, L., Hammer, B., & Schilling, M., 2021. Application of Graph Convolutions in a Lightweight Model for Skeletal Human Motion Forecasting. In ESANN 2021 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. . pp. 111-116.
    PUB | arXiv
     
  • [437]
    2021 | Konferenzbeitrag | Angenommen | PUB-ID: 2956774
    Hinder, F., & Hammer, B., Accepted. Concept Drift Segmentation via Kolmogorov Trees. In M. Verleysen, ed. Proceedings of the ESANN, 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.
    PUB
     
  • [436]
    2021 | Konferenzbeitrag | Angenommen | PUB-ID: 2955948
    Brinkrolf, J., & Hammer, B., Accepted. Federated Learning Vector Quantization. In M. Verleysen, ed. Proceedings of the ESANN, 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.
    PUB
     
  • [435]
    2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2952937 OA
    Kummert, J., et al., 2021. Efficient Reject Options for Particle Filter Object Tracking in Medical Applications. Sensors, 21(6): 2114.
    PUB | PDF | DOI | WoS | PubMed | Europe PMC
     
  • [434]
    2021 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2955115
    Straat, M., et al., 2021. Supervised learning in the presence of concept drift: a modelling framework. Neural Computing and Applications.
    PUB | DOI | WoS
     
  • [433]
    2020 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2958328
    Vaquet, V., & Hammer, B., 2020. Balanced SAM-kNN: Online Learning with Heterogeneous Drift and Imbalanced Data. In I. Farkaš, P. Masulli, & S. Wermter, eds. Artificial Neural Networks and Machine Learning – ICANN 2020. 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part II. Lecture Notes in Computer Science. no. 12397 Cham: Springer, pp. 850-862.
    PUB | DOI
     
  • [432]
    2020 | Konferenzbeitrag | PUB-ID: 2946488
    Hinder, F., Artelt, A., & Hammer, B., 2020. Towards non-parametric drift detection via Dynamic Adapting Window Independence Drift Detection (DAWIDD). In Proceedings of the 37th International Conference on Machine Learning.
    PUB | Download (ext.)
     
  • [431]
    2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2946685
    Artelt, A., & Hammer, B., 2020. Efficient computation of counterfactual explanations of LVQ models. In M. Verleysen, ed. ESANN 2020 - proceedings. Louvain-la-Neuve: Ciaco , pp. 19-24.
    PUB | Download (ext.)
     
  • [430]
    2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2946761
    Artelt, A., & Hammer, B., 2020. Convex Density Constraints for Computing Plausible Counterfactual Explanations. In I. Farkas, P. Masulli, & S. Wermter, eds. Artificial Neural Networks and Machine Learning - ICANN 2020 - 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15-18, 2020, Proceedings, Part {I}. Lecture Notes in Computer Science. no.12396 Cham: Springer, pp. 353-365.
    PUB | DOI | Download (ext.)
     
  • [429]
    2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2957814
    Krämer, N., et al., 2020. Improving and Evaluating Conversational User Interfaces for Children. In IUI '20: Proceedings of the 25th International Conference on Intelligent User Interfaces. New York: Association for Computing Machinery.
    PUB
     
  • [428]
    2020 | Konferenzbeitrag | PUB-ID: 2943260
    Schulz, A., Hinder, F., & Hammer, B., 2020. DeepView: Visualizing Classification Boundaries of Deep Neural Networks as Scatter Plots Using Discriminative Dimensionality Reduction. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, {IJCAI-20}.
    PUB | DOI | Download (ext.) | arXiv
     
  • [427]
    2020 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982081
    Biehl, M., et al., 2020. Prototype-Based Classifiers in the Presence of Concept Drift: A Modelling Framework. In A. Vellido, et al., eds. 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. Advances in Intelligent Systems and Computing. Cham: Springer International Publishing, pp. 210-221.
    PUB | DOI
     
  • [426]
    2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2940666
    Brinkrolf, J., & Hammer, B., 2020. Sparse Metric Learning in Prototype-based Classification. In M. Verleysen, ed. Proceedings of the ESANN, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. pp. 375-380.
    PUB
     
  • [425]
    2020 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2939517
    Pfannschmidt, L., et al., 2020. Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information. Neurocomputing.
    PUB | DOI | Download (ext.) | WoS | arXiv
     
  • [424]
    2020 | Report | Veröffentlicht | PUB-ID: 2946614 OA
    Hammer, B., et al., 2020. Sustainability and Trust for Artificial Intelligence Technologies,
    PUB | PDF | DOI
     
  • [423]
    2020 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2942892
    Iliadis, L.S., Kurkova, V., & Hammer, B., 2020. Brain-inspired computing and machine learning. NEURAL COMPUTING & APPLICATIONS.
    PUB | DOI | WoS
     
  • [422]
    2020 | Preprint | Entwurf | PUB-ID: 2942271 OA
    Pfannschmidt, L., & Hammer, B., Draft. Sequential Feature Classification in the Context of Redundancies.
    PUB | PDF | arXiv
     
  • [421]
    2019 | Preprint | PUB-ID: 2959898
    Artelt, A., & Hammer, B., 2019. On the computation of counterfactual explanations - A survey. arXiv: 1911.07749v1.
    PUB | Download (ext.) | arXiv
     
  • [420]
    2019 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982085
    Göpfert, J.P., Wersing, H., & Hammer, B., 2019. Recovering Localized Adversarial Attacks. In I. V. Tetko, et al., eds. 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. Lecture Notes in Computer Science. Cham: Springer International Publishing, pp. 302-311.
    PUB | DOI
     
  • [419]
    2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982084
    Losing, V., et al., 2019. Personalized Online Learning of Whole-Body Motion Classes using Multiple Inertial Measurement Units. In 2019 International Conference on Robotics and Automation (ICRA). IEEE, pp. 9530-9536.
    PUB | DOI
     
  • [418]
    2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982082
    Hosseini, B., & Hammer, B., 2019. Large-Margin Multiple Kernel Learning for Discriminative Features Selection and Representation Learning. In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 1-8.
    PUB | DOI
     
  • [417]
    2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982083
    Li, P., Niggemann, O., & Hammer, B., 2019. On the Identification of Decision Boundaries for Anomaly Detection in CPPS. In 2019 IEEE International Conference on Industrial Technology (ICIT). IEEE, pp. 1311-1316.
    PUB | DOI
     
  • [416]
    2019 | Monographie | PUB-ID: 2935200 OA
    Paaßen, B., Artelt, A., & Hammer, B., 2019. Lecture Notes on Applied Optimization, Faculty of Technology, Bielefeld University.
    PUB | Dateien verfügbar
     
  • [415]
    2019 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2934458 OA
    Prahm, C., et al., 2019. Counteracting Electrode Shifts in Upper-Limb Prosthesis Control via Transfer Learning. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(5), p 956-962.
    PUB | PDF | DOI | WoS | PubMed | Europe PMC
     
  • [414]
    2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2933893
    Pfannschmidt, L., et al., 2019. Feature Relevance Bounds for Ordinal Regression. In M. Verleysen, ed. Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019). Louvain-la-Neuve: i6doc.
    PUB | Download (ext.) | arXiv
     
  • [413]
    2019 | Konferenzbeitrag | Angenommen | PUB-ID: 2937842 OA
    Hosseini, B., & Hammer, B., Accepted. Deep-Aligned Convolutional Neural Network for Skeleton-based Action Recognition and Segmentation. Presented at the 2019 IEEE International Conference on Data Mining (ICDM), Beijing.
    PUB | Datei | arXiv
     
  • [412]
    2019 | Konferenzbeitrag | Angenommen | PUB-ID: 2937841 OA
    Hosseini, B., & Hammer, B., Accepted. Interpretable Multiple-Kernel Prototype Learning for Discriminative Representation and Feature Selection. Presented at the The 28th ACM International Conference on Information and Knowledge Management (CIKM) , Beijing.
    PUB | Datei | arXiv
     
  • [411]
    2019 | Report | Veröffentlicht | PUB-ID: 2937888
    Krämer, N., et al., 2019. KI-basierte Sprachassistenten im Alltag: Forschungsbedarf aus informatischer, psychologischer, ethischer und rechtlicher Sicht, Universität Duisburg-Essen, Universitätsbibliothek.
    PUB | DOI | Download (ext.)
     
  • [410]
    2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2937839 OA
    Hosseini, B., & Hammer, B., 2019. Interpretable Discriminative Dimensionality Reduction and Feature Selection on the Manifold. Presented at the 2019 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML), Würzburg.
    PUB | Datei | arXiv
     
  • [409]
    2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2935456 OA
    Pfannschmidt, L., et al., 2019. FRI - Feature Relevance Intervals for Interpretable and Interactive Data Exploration. Presented at the 16th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, Certosa di Pontignano, Siena - Tuscany, Italy.
    PUB | PDF | DOI | arXiv
     
  • [408]
    2019 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2933715 OA
    Brinkrolf, J., Göpfert, C., & Hammer, B., 2019. Differential privacy for learning vector quantization. Neurocomputing, 342, p 125-136.
    PUB | PDF | DOI | WoS
     
  • [407]
    2019 | Konferenzbeitrag | PUB-ID: 2930303
    Hosseini, B., & Hammer, B., 2019. Multiple-Kernel Dictionary Learning for Reconstruction and Clustering of Unseen Multivariate Time-series. In M. Verleysen, ed. Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019).
    PUB | arXiv
     
  • [406]
    2019 | Konferenzbeitrag | PUB-ID: 2934192
    Hosseini, B., & Hammer, B., 2019. 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.
    PUB | arXiv
     
  • [405]
    2019 | Preprint | Veröffentlicht | PUB-ID: 2934181
    Göpfert, J.P., Wersing, H., & Hammer, B., 2019. Adversarial attacks hidden in plain sight.
    PUB | DOI | arXiv
     
  • [404]
    2019 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2932914
    Brinkrolf, J., & Hammer, B., 2019. Time integration and reject options for probabilistic output of pairwise LVQ. Neural Computing and Applications.
    PUB | DOI | WoS
     
  • [403]
    2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2931283 OA
    Queißer, J., et al., 2018. Skill Memories for Parameterized Dynamic Action Primitives on the Pneumatically Driven Humanoid Robot Child Affetto. Presented at the International Conference on Development and Learning and on Epigenetic Robotics 2018 (ICDL-EPIROB2018), Tokyo .
    PUB | PDF
     
  • [402]
    2018 | Datenpublikation | PUB-ID: 2930611 OA
    Hülsmann, F., et al., 2018. Classification of motor errors to provide real-time feedback for sports coaching in virtual reality - A case study in squats and Tai Chi pushes (Data), Bielefeld University.
    PUB | Dateien verfügbar | DOI
     
  • [401]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2930862
    Hülsmann, F., et al., 2018. Classification of motor errors to provide real-time feedback for sports coaching in virtual reality — A case study in squats and Tai Chi pushes. Computers & Graphics, 76, p 47-59.
    PUB | DOI | Download (ext.) | WoS
     
  • [400]
    2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982092
    Queisser, J.F., et al., 2018. Skill Memories for Parameterized Dynamic Action Primitives on the Pneumatically Driven Humanoid Robot Child Affetto. In 2018 Joint IEEE 8th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob). IEEE, pp. 39-45.
    PUB | DOI
     
  • [399]
    2018 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982090
    Hosseini, B., & Hammer, B., 2018. Non-negative Local Sparse Coding for Subspace Clustering. In W. Duivesteijn, A. Siebes, & A. Ukkonen, eds. Advances in Intelligent Data Analysis XVII. 17th International Symposium, IDA 2018, ’s-Hertogenbosch, The Netherlands, October 24–26, 2018, Proceedings. Lecture Notes in Computer Science. Cham: Springer International Publishing, pp. 137-150.
    PUB | DOI
     
  • [398]
    2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982089
    Specht, F., et al., 2018. Generation of Adversarial Examples to Prevent Misclassification of Deep Neural Network based Condition Monitoring Systems for Cyber-Physical Production Systems. In 2018 IEEE 16th International Conference on Industrial Informatics (INDIN). IEEE, pp. 760-765.
    PUB | DOI
     
  • [397]
    2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982088
    Losing, V., Wersing, H., & Hammer, B., 2018. Enhancing Very Fast Decision Trees with Local Split-Time Predictions. In 2018 IEEE International Conference on Data Mining (ICDM). IEEE, pp. 287-296.
    PUB | DOI
     
  • [396]
    2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982087
    Hosseini, B., & Hammer, B., 2018. Confident Kernel Sparse Coding and Dictionary Learning. In 2018 IEEE International Conference on Data Mining (ICDM). IEEE, pp. 1031-1036.
    PUB | DOI
     
  • [395]
    2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982086
    Li, P., Niggemann, O., & Hammer, B., 2018. A Geometric Approach to Clustering Based Anomaly Detection for Industrial Applications. In IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society. IEEE, pp. 5345-5352.
    PUB | DOI
     
  • [394]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2932412
    Straat, M., et al., 2018. Statistical Mechanics of On-Line Learning Under Concept Drift. ENTROPY, 20(10): 775.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [393]
    2018 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2917896
    Lux, M., et al., 2018. flowLearn: Fast and precise identification and quality checking of cell populations in flow cytometry. Bioinformatics, 34(13), p 2245-2253.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [392]
    2018 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2933557
    Meyer, S., et al., 2018. Inferring Temporal Structure from Predictability in Bumblebee Learning Flight. In H. Yin, et al., eds. Intelligent Data Engineering and Automated Learning – IDEAL 2018. Lecture Notes in Computer Science. no.11314 Cham: Springer International Publishing, pp. 508-519.
    PUB | DOI
     
  • [391]
    2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2918254
    Brinkrolf, J., Berger, K., & Hammer, B., 2018. Differential private relevance learning. In M. Verleysen, ed. Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018). pp. 555-560.
    PUB | Download (ext.)
     
  • [390]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2911900
    Paaßen, B., Göpfert, C., & Hammer, B., 2018. Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces. Neural Processing Letters, 48(2), p 669-689.
    PUB | DOI | Download (ext.) | WoS | arXiv
     
  • [389]
    2018 | Preprint | Veröffentlicht | PUB-ID: 2921209 OA
    Hosseini, B., & Hammer, B., 2018. Non-Negative Local Sparse Coding for Subspace Clustering. Advances in Intelligent Data Analysis XVII. IDA 2018.
    PUB | Datei | Download (ext.) | arXiv
     
  • [388]
    2018 | Konferenzbeitrag | Im Druck | PUB-ID: 2932116 OA
    Hosseini, B., & Hammer, B., In Press. Confident Kernel Sparse Coding and Dictionary Learning. In 2018 IEEE International Conference on Data Mining (ICDM).
    PUB | Datei | arXiv
     
  • [387]
    2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2919598
    Hosseini, B., & Hammer, B., 2018. Feasibility Based Large Margin Nearest Neighbor Metric Learning. In ESANN 2018. Proceedings of 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. pp. 219-224.
    PUB | arXiv
     
  • [386]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2914505
    Paaßen, B., et al., 2018. Expectation maximization transfer learning and its application for bionic hand prostheses. Neurocomputing, 298, p 122-133.
    PUB | DOI | Download (ext.) | WoS | arXiv
     
  • [385]
    2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2921316 OA
    Göpfert, J.P., Hammer, B., & Wersing, H., 2018. Mitigating Concept Drift via Rejection. In V. Kurkova, et al., eds. Artificial Neural Networks and Machine Learning – ICANN 2018. Proceedings, Part I. Lecture Notes in Computer Science. no.11139 Cham: Springer.
    PUB | PDF | DOI
     
  • [384]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2917201
    Losing, V., Hammer, B., & Wersing, H., 2018. Tackling heterogeneous concept drift with the Self-Adjusting Memory (SAM). KNOWLEDGE AND INFORMATION SYSTEMS, 54(1), p 171-201.
    PUB | DOI | WoS
     
  • [383]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2915273 OA
    Göpfert, C., et al., 2018. Interpretation of Linear Classifiers by Means of Feature Relevance Bounds. Neurocomputing, 298, p 69-79.
    PUB | PDF | DOI | WoS
     
  • [382]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2913389
    Paaßen, B., et al., 2018. The Continuous Hint Factory - Providing Hints in Vast and Sparsely Populated Edit Distance Spaces. Journal of Educational Data Mining, 10(1), p 1-35.
    PUB | Download (ext.) | arXiv
     
  • [381]
    2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2919844
    Paaßen, B., et al., 2018. Tree Edit Distance Learning via Adaptive Symbol Embeddings. In J. Dy & A. Krause, eds. Proceedings of the 35th International Conference on Machine Learning (ICML 2018). Proceedings of Machine Learning Research. no.80 pp. 3973-3982.
    PUB | Download (ext.) | arXiv
     
  • [380]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2914730 OA
    Losing, V., Hammer, B., & Wersing, H., 2018. Incremental on-line learning: A review and comparison of state of the art algorithms. Neurocomputing, 275, p 1261-1274.
    PUB | PDF | DOI | WoS
     
  • [379]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2918244
    Brinkrolf, J., & Hammer, B., 2018. Interpretable Machine Learning with Reject Option. at - Automatisierungstechnik, 66(4), p 283-290.
    PUB | DOI | WoS
     
  • [378]
    2018 | Konferenzbeitrag | PUB-ID: 2916318
    Berger, K., et al., 2018. Linear Supervised Transfer Learning for the Large Margin Nearest Neighbor Classifier. Presented at the SSCI CIDM 2017
    PUB | DOI
     
  • [377]
    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982095
    Frenay, B., & Hammer, B., 2017. Label-noise-tolerant classification for streaming data. In 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 1748-1755.
    PUB | DOI
     
  • [376]
    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982091
    Losing, V., Hammer, B., & Wersing, H., 2017. Personalized maneuver prediction at intersections. In 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC). IEEE, pp. 1-6.
    PUB | DOI
     
  • [375]
    2017 | Kurzbeitrag Konferenz / Poster | PUB-ID: 2919987 OA
    Hosseini, B., & Hammer, B., 2017. Non-negative Kernel Sparse Coding Frameworks for Efficient Analysis of Motion Data. Presented at the BMVA Symposium on Human Activity Recognition and Monitoring, London.
    PUB | PDF
     
  • [374]
    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909369 OA
    Paaßen, B., et al., 2017. An EM transfer learning algorithm with applications in bionic hand prostheses. In M. Verleysen, ed. Proceedings of the 25th European Symposium on Artificial Neural Networks (ESANN 2017). Bruges: i6doc.com, pp. 129-134.
    PUB | PDF
     
  • [373]
    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2914945
    Brinkrolf, J., & Hammer, B., 2017. Probabilistic extension and reject options for pairwise LVQ. In 2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM). Piscataway, NJ: IEEE.
    PUB | DOI
     
  • [372]
    2017 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2909372 OA
    Schulz, A., Brinkrolf, J., & Hammer, B., 2017. Efficient Kernelization of Discriminative Dimensionality Reduction. Neurocomputing, 268(SI), p 34-41.
    PUB | PDF | DOI | WoS
     
  • [371]
    2017 | Konferenzbeitrag | PUB-ID: 2909371
    Biehl, M., Hammer, B., & Villmann, T., 2017. Prototype based models for the supervised learning of classificaton schemes. In Proc. of the IAU Symposium 325 on Astroinformatics, Sorrento/Italy, October 2016. pp. in press.
    PUB
     
  • [370]
    2017 | Konferenzbeitrag | PUB-ID: 2914950
    Brinkrolf, J., Berger, K., & Hammer, B., 2017. Differential Privacy for Learning Vector Quantization. In New Challenges in Neural Computation.
    PUB
     
  • [369]
    2017 | Konferenzbeitrag | PUB-ID: 2914734 OA
    Losing, V., Hammer, B., & Wersing, H., 2017. Self-Adjusting Memory: How to Deal with Diverse Drift Types. Presented at the International Joint Conference on Artificial Intelligence (IJCAI) 2017, Melbourne.
    PUB | PDF | DOI
     
  • [368]
    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2908201 OA
    Göpfert, C., Pfannschmidt, L., & Hammer, B., 2017. Feature Relevance Bounds for Linear Classification. In M. Verleysen, ed. Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Louvain-la-Neuve: Ciaco - i6doc.com, pp. 187--192.
    PUB | Dateien verfügbar | Download (ext.)
     
  • [367]
    2017 | Konferenzbeitrag | PUB-ID: 2914732 OA
    Losing, V., Hammer, B., & Wersing, H., 2017. Personalized Maneuver Prediction at Intersections. Presented at the IEEE Intelligent Transportation Systems Conference 2017, Yokohama.
    PUB | PDF
     
  • [366]
    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2913752 OA
    Göpfert, J.P., et al., 2017. Effects of Variability in Synthetic Training Data on Convolutional Neural Networks for 3D Head Reconstruction. In 2017 SSCI Proceedings. 2017 IEEE Symposium Series on Computational Intelligence (SSCI). Piscataway, NJ: IEEE.
    PUB | PDF | DOI
     
  • [365]
    2017 | Kurzbeitrag Konferenz / Poster | PUB-ID: 2919990 OA
    Hosseini, B., & Hammer, B., 2017. Task-Driven Sparse Coding for Classification of Motion Data. Presented at the Ninth Mittweida Workshop on Computational Intelligence (MiWoCI 2017), Mittweida.
    PUB | PDF
     
  • [364]
    2017 | Konferenzbeitrag | PUB-ID: 2909370
    Frenay, B., & Hammer, B., 2017. Label-Noise-Tolerant Classification for Streaming Data. In IEEE International Joint Conference on Neural Neworks.
    PUB
     
  • [363]
    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2914141 OA
    Aswolinskiy, W., & Hammer, B., 2017. Unsupervised Transfer Learning for Time Series via Self-Predictive Modelling - First Results. In Proceedings of the Workshop on New Challenges in Neural Computation (NC2). Machine Learning Reports. no.03/2017 Bielefeld: Universität Bielefeld, CITEC.
    PUB | PDF
     
  • [362]
    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909037 OA
    Prahm, C., et al., 2017. Echo State Networks as Novel Approach for Low-Cost Myoelectric Control. In A. ten Telje, et al., eds. Proceedings of the 16th Conference on Artificial Intelligence in Medicine (AIME 2017). Lecture Notes in Computer Science. no.10259 Springer, pp. 338--342.
    PUB | Dateien verfügbar | DOI
     
  • [361]
    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2915274 OA
    Göpfert, C., Göpfert, J.P., & Hammer, B., 2017. Analyzing Feature Relevance for Linear Reject Option SVM using Relevance Intervals. In Proceedings of the 2017 NIPS workshop on Transparent and Interpretable Machine Learning in Safety Critical Environments.
    PUB | PDF
     
  • [360]
    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2904469
    Hosseini, B., et al., 2016. Non-Negative Kernel Sparse Coding for the Analysis of Motion Data. In A. E.P. Villa, P. Masulli, & A. Javier Pons Rivero, eds. Artificial Neural Networks and Machine Learning – ICANN 2016. Lecture Notes in Computer Science. no.9887 Cham: Springer, pp. 506-514.
    PUB | PDF | DOI | Download (ext.) | arXiv
     
  • [359]
    2016 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2982097
    Biehl, M., Hammer, B., & Villmann, T., 2016. Prototype-based Models for the Supervised Learning of Classification Schemes. Proceedings of the International Astronomical Union, 12(S325), p 129-138.
    PUB | DOI
     
  • [358]
    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982096
    Fischer, L., Hammer, B., & Wersing, H., 2016. Online metric learning for an adaptation to confidence drift. In 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 748-755.
    PUB | DOI
     
  • [357]
    2016 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2907633 OA
    Lux, M., et al., 2016. acdc – Automated Contamination Detection and Confidence estimation for single-cell genome data. BMC Bioinformatics, 17(1): 543.
    PUB | PDF | DOI | WoS | PubMed | Europe PMC
     
  • [356]
    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909367
    Kummert, J., et al., 2016. Local Reject Option for Deterministic Multi-class SVM. In A. E.P. Villa, P. Masulli, & A. J. Pons Rivero, eds. Artificial Neural Networks and Machine Learning - ICANN 2016 - 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II. Lecture Notes in Computer Science. no.9887 Cham: Springer Nature, pp. 251--258.
    PUB | DOI
     
  • [355]
    2016 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2783224 OA
    Paaßen, B., Mokbel, B., & Hammer, B., 2016. Adaptive structure metrics for automated feedback provision in intelligent tutoring systems. Neurocomputing, 192(SI), p 3-13.
    PUB | PDF | DOI | WoS
     
  • [354]
    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2904509
    Paaßen, B., Jensen, J., & Hammer, B., 2016. Execution Traces as a Powerful Data Representation for Intelligent Tutoring Systems for Programming. In T. Barnes, M. Chi, & M. Feng, eds. Proceedings of the 9th International Conference on Educational Data Mining. Raleigh, North Carolina, USA: International Educational Datamining Society, pp. 183-190.
    PUB | Download (ext.)
     
  • [353]
    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900676 OA
    Paaßen, B., Göpfert, C., & Hammer, B., 2016. Gaussian process prediction for time series of structured data. In M. Verleysen, ed. Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Louvain-la-Neuve: Ciaco - i6doc.com, pp. 41--46.
    PUB | PDF
     
  • [352]
    2016 | Konferenzbeitrag | E-Veröff. vor dem Druck | PUB-ID: 2904909 OA
    Schulz, A., & Hammer, B., 2016. Discriminative Dimensionality Reduction in Kernel Space. In ESANN2016 Proceedings. 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges, Belgium,27-29 April 2016. i6doc.com.
    PUB | PDF
     
  • [351]
    2016 | Konferenzbeitrag | PUB-ID: 2909365
    Brinkrolf, J., et al., 2016. Virtual optimisation for improved production planning. In New Challenges in Neural Computation.
    PUB
     
  • [350]
    2016 | Konferenzbeitrag | PUB-ID: 2907624 OA
    Losing, V., Hammer, B., & Wersing, H., 2016. Choosing the Best Algorithm for an Incremental On-line Learning Task. Presented at the European Symposium on Artificial Neural Networks, Brügge.
    PUB | PDF
     
  • [349]
    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2905729 OA
    Göpfert, C., Paaßen, B., & Hammer, B., 2016. Convergence of Multi-pass Large Margin Nearest Neighbor Metric Learning. In A. E.P. Villa, P. Masulli, & A. J. Pons Rivero, eds. Artificial Neural Networks and Machine Learning – ICANN 2016: 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II. Lecture Notes in Computer Science. no.9887 Cham: Springer Nature, pp. 510-517.
    PUB | PDF | DOI
     
  • [348]
    2016 | Konferenzbeitrag | PUB-ID: 2908455 OA
    Losing, V., Hammer, B., & Wersing, H., 2016. Dedicated Memory Models for Continual Learning in the Presence of Concept Drift. Presented at the Continual Learning Workshop of the Thirtieth Annual Conference on Neural Information Processing Systems (NIPS), Barcelona.
    PUB | PDF
     
  • [347]
    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2905855
    Paaßen, B., Schulz, A., & Hammer, B., 2016. Linear Supervised Transfer Learning for Generalized Matrix LVQ. In B. Hammer, T. Martinetz, & T. Villmann, eds. Proceedings of the Workshop New Challenges in Neural Computation 2016. Machine Learning Reports. pp. 11-18.
    PUB | Download (ext.)
     
  • [346]
    2016 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2903457
    Schleif, F.-M., et al., 2016. Odor recognition in robotics applications by discriminative time-series modeling. PATTERN ANALYSIS AND APPLICATIONS, 19(1), p 207-220.
    PUB | DOI | WoS
     
  • [345]
    2016 | Konferenzbeitrag | PUB-ID: 2909368
    Geppert, er, & Hammer, B., 2016. Incremental learning algorithms and applications. In ESANN.
    PUB
     
  • [344]
    2016 | Konferenzbeitrag | PUB-ID: 2905195
    Fischer, L., Hammer, B., & Wersing, H., 2016. Online Metric Learning for an Adaptation to Confidence Drift. In Proceedings of International Joint Conference on Neural Networks (IJCNN). Vancouver: IEEE, pp. 748-755.
    PUB
     
  • [343]
    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2904178 OA
    Prahm, C., et al., 2016. Transfer Learning for Rapid Re-calibration of a Myoelectric Prosthesis after Electrode Shift. In J. Ibáñez, et al., eds. Converging Clinical and Engineering Research on Neurorehabilitation II: Proceedings of the 3rd International Conference on NeuroRehabilitation (ICNR2016). Springer, pp. 153--157.
    PUB | PDF | DOI | Download (ext.)
     
  • [342]
    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2907622 OA
    Losing, V., Hammer, B., & Wersing, H., 2016. KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift. In 2016 IEEE 16th International Conference on Data Mining (ICDM). Piscataway, NJ: IEEE, pp. 291-300.
    PUB | PDF | DOI
     
  • [341]
    2016 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2910957
    Biehl, M., Hammer, B., & Villmann, T., 2016. Prototype-based models in machine learning. Wiley Interdisciplinary Reviews: Cognitive Science, 7(2), p 92-111.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [340]
    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909366
    Villmann, T., et al., 2016. Self-Adjusting Reject Options in Prototype Based Classification. In E. Merényi, M. J. Mendenhall, & P. O'Driscoll, eds. Advances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 11th International Workshop WSOM 2016, Houston, Texas, USA, January 6-8, 2016. Advances in Intelligent Systems and Computing. no.428 Cham: Springer International Publishing, pp. 269-279.
    PUB | DOI
     
  • [339]
    2016 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2905193
    Fischer, L., Hammer, B., & Wersing, H., 2016. Optimal local rejection for classifiers. Neurocomputing, 214, p 445-457.
    PUB | DOI | WoS
     
  • [338]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982098
    Fischer, L., Hammer, B., & Wersing, H., 2015. Combining offline and online classifiers for life-long learning. In 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 1-8.
    PUB | DOI
     
  • [337]
    2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2752948 OA
    Gross, S., et al., 2015. Learning Feedback in Intelligent Tutoring Systems. Report of the FIT Project, Conducted from December 2011 to March 2015. KI - Künstliche Intelligenz, 29(4), p 413-418.
    PUB | PDF | DOI | Download (ext.) | WoS
     
  • [336]
    2015 | Preprint | Veröffentlicht | PUB-ID: 2901613
    Lux, M., Hammer, B., & Sczyrba, A., 2015. Automated Contamination Detection in Single-Cell Sequencing. bioRxiv.
    PUB
     
  • [335]
    2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2671047 OA
    Gisbrecht, A., Schulz, A., & Hammer, B., 2015. Parametric nonlinear dimensionality reduction using kernel t-SNE. Neurocomputing, 147, p 71-82.
    PUB | PDF | DOI | WoS
     
  • [334]
    2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2909226
    Gisbrecht, A., & Hammer, B., 2015. Data visualization by nonlinear dimensionality reduction. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 5(2), p 51-73.
    PUB | DOI | WoS
     
  • [333]
    2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2759763
    Schleif, F.-M., Zhu, X., & Hammer, B., 2015. Sparse conformal prediction for dissimilarity data. Annals of Mathematics and Artificial Intelligence, 74(1-2), p 95-116.
    PUB | DOI | WoS
     
  • [332]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2783165
    Hosseini, B., & Hammer, B., 2015. Efficient Metric Learning for the Analysis of Motion Data. In 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA). Piscataway, NJ: IEEE.
    PUB | DOI | Download (ext.) | arXiv
     
  • [331]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2903777 OA
    Schulz, A., et al., 2015. Inferring Feature Relevances From Metric Learning. In 2015 IEEE Symposium Series on Computational Intelligence. Piscataway, NJ: IEEE.
    PUB | PDF | DOI
     
  • [330]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2724156 OA
    Paaßen, B., Mokbel, B., & Hammer, B., 2015. Adaptive structure metrics for automated feedback provision in Java programming. In M. Verleysen, ed. Proceedings of the ESANN, 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. pp. 307-312.
    PUB | PDF
     
  • [329]
    2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2710031 OA
    Mokbel, B., et al., 2015. Metric learning for sequences in relational LVQ. Neurocomputing, 169(SI), p 306-322.
    PUB | PDF | DOI | Download (ext.) | WoS
     
  • [328]
    2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2766822 OA
    Schulz, A., Gisbrecht, A., & Hammer, B., 2015. Using Discriminative Dimensionality Reduction to Visualize Classifiers. Neural Processing Letters, 42(1), p 27-54.
    PUB | PDF | DOI | WoS
     
  • [327]
    2015 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2900303 OA
    Schulz, A., & Hammer, B., 2015. Visualization of Regression Models Using Discriminative Dimensionality Reduction. In Computer Analysis of Images and Patterns. Lecture Notes in Computer Science. no.9257 Cham: Springer Science + Business Media, pp. 437-449.
    PUB | PDF | DOI
     
  • [326]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900325 OA
    Blöbaum, P., Schulz, A., & Hammer, B., 2015. Unsupervised Dimensionality Reduction for Transfer Learning. In M. Verleysen, ed. Proceedings. 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Louvain-la-Neuve: Ciaco, pp. 507-512.
    PUB | PDF
     
  • [325]
    2015 | Zeitschriftenaufsatz | PUB-ID: 2909364
    Hammer, B., & Toussaint, M., 2015. Special Issue on Autonomous Learning. {KI}, 29(4), p 323--327.
    PUB | DOI | WoS
     
  • [324]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900319
    Schulz, A., & Hammer, B., 2015. Discriminative dimensionality reduction for regression problems using the Fisher metric. In 2015 International Joint Conference on Neural Networks (IJCNN). Institute of Electrical & Electronics Engineers (IEEE), pp. 1-8.
    PUB | DOI
     
  • [323]
    2015 | Preprint | PUB-ID: 2774656
    Fischer, L., Hammer, B., & Wersing, H., 2015. Optimum Reject Options for Prototype-based Classification.
    PUB | arXiv
     
  • [322]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2774707
    Fischer, L., Hammer, B., & Wersing, H., 2015. Certainty-based Prototype Insertion/Deletion for Classification with Metric Adaptation. In ESANN, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. pp. 7-12.
    PUB
     
  • [321]
    2015 | Konferenzbeitrag | PUB-ID: 2774721
    Fischer, L., Hammer, B., & Wersing, H., 2015. Combining Offline and Online Classifiers for Life-long Learning. In IJCNN, International Joint Conference on Neural Networks. pp. 2808-2815.
    PUB
     
  • [320]
    2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2772407
    Nebel, D., et al., 2015. Median variants of learning vector quantization for learning of dissimilarity data. Neurocomputing, 169(SI), p 295-305.
    PUB | DOI | WoS
     
  • [319]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2762087
    Paaßen, B., Mokbel, B., & Hammer, B., 2015. A Toolbox for Adaptive Sequence Dissimilarity Measures for Intelligent Tutoring Systems. In O. C. Santos, et al., eds. Proceedings of the 8th International Conference on Educational Data Mining. International Educational Datamining Society, pp. 632-632.
    PUB | Download (ext.)
     
  • [318]
    2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2752955 OA
    Walter, O., et al., 2015. Autonomous Learning of Representations. KI - Künstliche Intelligenz, 29(4), p 339–351.
    PUB | PDF | DOI | Download (ext.) | WoS
     
  • [317]
    2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2695196
    Hofmann, D., Gisbrecht, A., & Hammer, B., 2015. Efficient approximations of robust soft learning vector quantization for non-vectorial data. Neurocomputing, 147, p 96-106.
    PUB | DOI | WoS
     
  • [316]
    2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2772413
    Fischer, L., Hammer, B., & Wersing, H., 2015. Efficient rejection strategies for prototype-based classification. Neurocomputing, 169(SI), p 334-342.
    PUB | DOI | WoS
     
  • [315]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2901612
    Lux, M., Sczyrba, A., & Hammer, B., 2015. Automatic discovery of metagenomic structure. In 2015 International Joint Conference on Neural Networks (IJCNN). Institute of Electrical & Electronics Engineers (IEEE).
    PUB | DOI
     
  • [314]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900318
    Schulz, A., & Hammer, B., 2015. Metric Learning in Dimensionality Reduction. In Proceedings of the International Conference on Pattern Recognition Applications and Methods. Scitepress, pp. 232-239.
    PUB | DOI
     
  • [313]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2776021 OA
    Losing, V., Hammer, B., & Wersing, H., 2015. Interactive Online Learning for Obstacle Classification on a Mobile Robot. Presented at the International Joint Conference on Neural Networks, Killarney, Ireland.
    PUB | PDF | DOI
     
  • [312]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2910954
    Biehl, M., et al., 2015. Stationarity of Matrix Relevance LVQ. In 2015 International Joint Conference on Neural Networks (IJCNN). IEEE.
    PUB | DOI
     
  • [311]
    2014 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982100
    Gross, S., et al., 2014. How to Select an Example? A Comparison of Selection Strategies in Example-Based Learning. In S. Trausan-Matu, et al., eds. Intelligent Tutoring Systems. Lecture Notes in Computer Science. Cham: Springer International Publishing, pp. 340-347.
    PUB | DOI
     
  • [310]
    2014 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982099
    Biehl, M., Hammer, B., & Villmann, T., 2014. Distance Measures for Prototype Based Classification. In L. Grandinetti, T. Lippert, & N. Petkov, eds. Brain-Inspired Computing. International Workshop, BrainComp 2013, Cetraro, Italy, July 8-11, 2013, Revised Selected Papers. Lecture Notes in Computer Science. Cham: Springer International Publishing, pp. 100-116.
    PUB | DOI
     
  • [309]
    2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900320 OA
    Frenay, B., et al., 2014. Valid interpretation of feature relevance for linear data mappings. In 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM). Piscataway, NJ: Institute of Electrical & Electronics Engineers (IEEE), pp. 149-156.
    PUB | PDF | DOI
     
  • [308]
    2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2678214
    Hofmann, D., et al., 2014. Learning interpretable kernelized prototype-based models. Neurocomputing, 141, p 84-96.
    PUB | DOI | Download (ext.) | WoS
     
  • [307]
    2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2672504
    Zhu, X., Schleif, F.-M., & Hammer, B., 2014. Adaptive Conformal Semi-Supervised Vector Quantization for Dissimilarity Data. Pattern Recognition Letters, 49, p 138-145.
    PUB | DOI | WoS
     
  • [306]
    2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2615730
    Hammer, B., et al., 2014. Learning vector quantization for (dis-)similarities. NeuroComputing, 131, p 43-51.
    PUB | DOI | WoS
     
  • [305]
    2014 | Konferenzbeitrag | PUB-ID: 2909360
    Gross, S., et al., 2014. How to Select an Example? A Comparison of Selection Strategies in Example-Based Learning. In S. Trausan-Matu, et al., eds. Intelligent Tutoring Systems. Lecture Notes in Computer Science. no.8474 Springer, pp. 340-347.
    PUB
     
  • [304]
    2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2694967
    Jin, Y., & Hammer, B., 2014. Computational Intelligence in Big Data. IEEE Computational Intelligence Magazine, 9(3), p 12-13.
    PUB | DOI | WoS
     
  • [303]
    2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2774643
    Fischer, L., et al., 2014. Rejection Strategies for Learning Vector Quantization – A Comparison of Probabilistic and Deterministic Approaches. In T. Villmann, et al., eds. Advances in Self-Organizing Maps and Learning Vector Quantization. Advances in Intelligent Systems and Computing. no.295 Cham: Springer International Publishing, pp. 109-118.
    PUB | DOI
     
  • [302]
    2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2673548
    Fischer, L., Hammer, B., & Wersing, H., 2014. Rejection strategies for learning vector quantization. In M. Verleysen, ed. ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges, Belgium: i6doc.com, pp. 41-46.
    PUB
     
  • [301]
    2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2774498
    Fischer, L., Hammer, B., & Wersing, H., 2014. Local Rejection Strategies for Learning Vector Quantization. In S. Wermter, et al., eds. Artificial Neural Networks and Machine Learning – ICANN 2014. Lecture Notes in Computer Science. no.8681 Cham: Springer International Publishing, pp. 563-570.
    PUB | DOI
     
  • [300]
    2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2673554 OA
    Mokbel, B., Paaßen, B., & Hammer, B., 2014. Adaptive distance measures for sequential data. In M. Verleysen, ed. ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges, Belgium: i6doc.com, pp. 265-270.
    PUB | PDF
     
  • [299]
    2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2673559
    Hammer, B., He, H., & Martinetz, T., 2014. Learning and modeling big data. In M. Verleysen, ed. ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges, Belgium: i6doc.com, pp. 343-352.
    PUB
     
  • [298]
    2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2734058
    Gross, S., et al., 2014. Example-based feedback provision using structured solution spaces. International Journal of Learning Technology, 9(3), p 248-280.
    PUB | DOI | Download (ext.)
     
  • [297]
    2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2710067 OA
    Mokbel, B., Paaßen, B., & Hammer, B., 2014. Efficient Adaptation of Structure Metrics in Prototype-Based Classification. In S. Wermter, et al., eds. Artificial Neural Networks and Machine Learning - ICANN 2014 - 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings. Lecture Notes in Computer Science. no.8681 Springer, pp. 571-578.
    PUB | PDF | DOI | Download (ext.)
     
  • [296]
    2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2673545
    Nebel, D., Hammer, B., & Villmann, T., 2014. Supervised Generative Models for Learning Dissimilarity Data. In M. Verleysen, ed. ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges, Belgium: i6doc.com, pp. 35-40.
    PUB
     
  • [295]
    2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2673557
    Schulz, A., Gisbrecht, A., & Hammer, B., 2014. Relevance learning for dimensionality reduction. In M. Verleysen, ed. ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges, Belgium: i6doc.com, pp. 165-170.
    PUB
     
  • [294]
    2014 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2900324
    Gisbrecht, A., Schulz, A., & Hammer, B., 2014. Discriminative Dimensionality Reduction for the Visualization of Classifiers. In Pattern Recognition Applications and Methods. Advances in Intelligent Systems and Computing. no.318 Cham: Springer Science + Business Media, pp. 39-56.
    PUB | DOI
     
  • [293]
    2014 | Konferenzbeitrag | PUB-ID: 2909361
    Hammer, B., et al., 2014. Generative versus Discriminative Prototype Based Classification. In Advances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 10th International Workshop, {WSOM} 2014, Mittweida, Germany, July, 2-4, 2014. Cham: Springer International Publishing, pp. 123--132.
    PUB | DOI
     
  • [292]
    2013 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982105
    Schleif, F.-M., Zhu, X., & Hammer, B., 2013. Sparse Prototype Representation by Core Sets. In H. Yin, et al., eds. Intelligent Data Engineering and Automated Learning – IDEAL 2013. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 302-309.
    PUB | DOI
     
  • [291]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982104
    Strickert, M., et al., 2013. Regularization and improved interpretation of linear data mappings and adaptive distance measures. In 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM). IEEE, pp. 10-17.
    PUB | DOI
     
  • [290]
    2013 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982102
    Hofmann, D., Gisbrecht, A., & Hammer, B., 2013. Efficient Approximations of Kernel Robust Soft LVQ. In P. A. Estévez, J. C. Príncipe, & P. Zegers, eds. Advances in Self-Organizing Maps. Advances in Intelligent Systems and Computing. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 183-192.
    PUB | DOI
     
  • [289]
    2013 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982101
    Nebel, D., Hammer, B., & Villmann, T., 2013. A Median Variant of Generalized Learning Vector Quantization. In M. Lee, et al., eds. Neural Information Processing. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 19-26.
    PUB | DOI
     
  • [288]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2623500
    Gisbrecht, A., et al., 2013. Nonlinear dimensionality reduction for cluster identification in metagenomic samples. In E. Banissi, ed. 17th International Conference on Information Visualisation IV 2013. Piscataway, NJ: IEEE, pp. 174-179.
    PUB | DOI
     
  • [287]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622454
    Hammer, B., Gisbrecht, A., & Schulz, A., 2013. Applications of discriminative dimensionality reduction. In Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods. SCITEPRESS, pp. 33-41.
    PUB | DOI
     
  • [286]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625185
    Mokbel, B., et al., 2013. Domain-Independent Proximity Measures in Intelligent Tutoring Systems. In S. K. D'Mello, R. A. Calvo, & A. Olney, eds. Proceedings of the 6th International Conference on Educational Data Mining (EDM). pp. 334-335.
    PUB | Download (ext.)
     
  • [285]
    2013 | Konferenzbeitrag | PUB-ID: 2909358
    Strickert, M., et al., 2013. Regularization and Improved Interpretation of Linear Data Mappings and Adaptive Distance Measures. In IEEE SSCI CIDM 2013. IEEE Computational Intelligence Society, pp. 10-17.
    PUB
     
  • [284]
    2013 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2612736
    Mokbel, B., et al., 2013. Visualizing the quality of dimensionality reduction. Neurocomputing, 112, p 109-123.
    PUB | DOI | WoS
     
  • [283]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622456
    Schulz, A., Gisbrecht, A., & Hammer, B., 2013. Using Nonlinear Dimensionality Reduction to Visualize Classifiers. In I. Rojas, G. Joya, & J. Gabestany, eds. Advances in computational intelligence. Proceedings. Vol 1. Lecture Notes in Computer Science. no.7902 Springer, pp. 59-68.
    PUB | DOI | WoS
     
  • [282]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2670686
    Gross, S., et al., 2013. Towards a Domain-Independent ITS Middleware Architecture. In 2013 IEEE 13th International Conference on Advanced Learning Technologies. IEEE, pp. 408-409.
    PUB | DOI | WoS
     
  • [281]
    2013 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2607146
    Hammer, B., et al., 2013. Preface: Intelligent interactive data visualization. Data Mining and Knowledge Discovery, 27(1), p 1-3.
    PUB | DOI | WoS
     
  • [280]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622467
    Schulz, A., Gisbrecht, A., & Hammer, B., 2013. Classifier inspection based on different discriminative dimensionality reductions. In Workshop NC^2 2013. TR Machine Learning Reports, pp. 77-86.
    PUB
     
  • [279]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625194
    Gisbrecht, A., et al., 2013. Visualizing Dependencies of Spectral Features using Mutual Information. In ESANN, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. pp. 573-578.
    PUB
     
  • [278]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625199
    Hofmann, D., & Hammer, B., 2013. Sparse approximations for kernel learning vector quantization. In ESANN.
    PUB
     
  • [277]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625202
    Schleif, F.-M., Zhu, X., & Hammer, B., 2013. Sparse prototype representation by core sets. In et.al Hujun Yin, ed. IDEAL 2013.
    PUB
     
  • [276]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625207
    Gross, S., et al., 2013. Towards Providing Feedback to Students in Absence of Formalized Domain Models. In AIED. pp. 644-648.
    PUB
     
  • [275]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615717
    Zhu, X., Schleif, F.-M., & Hammer, B., 2013. Secure Semi-supervised Vector Quantization for Dissimilarity Data. In I. Rojas, G. Joya, & J. Cabestany, eds. IWANN (1). Lecture Notes in Computer Science. no.7902 Springer, pp. 347-356.
    PUB | DOI
     
  • [274]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615701
    Zhu, X., Schleif, F.-M., & Hammer, B., 2013. Semi-Supervised Vector Quantization for proximity data. In Proceedings of ESANN 2013. pp. 89-94.
    PUB
     
  • [273]
    2013 | Konferenzbeitrag | PUB-ID: 2909359
    Nebel, D., Hammer, B., & Villmann, T., 2013. A Median Variant of Generalized Learning Vector Quantization. In ICONIP (2). pp. 19-26.
    PUB
     
  • [272]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982108
    Gisbrecht, A., Mokbel, B., & Hammer, B., 2012. Linear basis-function t-SNE for fast nonlinear dimensionality reduction. In The 2012 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 1-8.
    PUB | DOI
     
  • [271]
    2012 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982106
    Gisbrecht, A., Hofmann, D., & Hammer, B., 2012. Discriminative Dimensionality Reduction Mappings. In J. Hollmén, F. Klawonn, & A. Tucker, eds. Advances in Intelligent Data Analysis XI. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 126-138.
    PUB | DOI
     
  • [270]
    2012 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982107
    Hofmann, D., & Hammer, B., 2012. Kernel Robust Soft Learning Vector Quantization. In N. Mana, F. Schwenker, & E. Trentin, eds. Artificial Neural Networks in Pattern Recognition. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 14-23.
    PUB | DOI
     
  • [269]
    2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2625232
    Gisbrecht, A., et al., 2012. Linear Time Relational Prototype Based Learning. International Journal of Neural Systems, 22(05): 1250021.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [268]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622449
    Schulz, A., et al., 2012. How to visualize a classifier? In Proceedings of the Workshop - New Challenges in Neural Computation 2012. Machine Learning Reports, pp. 73-83.
    PUB
     
  • [267]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625260
    Gisbrecht, A., et al., 2012. Out-of-sample kernel extensions for nonparametric dimensionality reduction. In ESANN 2012. pp. 531-536.
    PUB
     
  • [266]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625265
    Gisbrecht, A., et al., 2012. Relevance learning for time series inspection. In M. Verleysen, ed. ESANN 2012. pp. 489-494.
    PUB
     
  • [265]
    2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2625225
    Hammer, B., 2012. Special Issue on Neural Learning Paradigms. Künstliche Intelligenz :KI, 26(4), p 329-332.
    PUB | DOI
     
  • [264]
    2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2509858
    Kaestner, M., et al., 2012. Functional relevance learning in generalized learning vector quantization. Neurocomputing, 90, p 85-95.
    PUB | DOI | WoS
     
  • [263]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2671172
    Hofmann, D., Gisbrecht, A., & Hammer, B., 2012. Discriminative probabilistic prototype based models in kernel space. In Workshop NC^2 2012. TR Machine Learning Reports.
    PUB
     
  • [262]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2536426 OA
    Mokbel, B., et al., 2012. How to Quantitatively Compare Data Dissimilarities for Unsupervised Machine Learning? In N. Mana, F. Schwenker, & E. Trentin, eds. Artificial Neural Networks in Pattern Recognition. 5th INNS IAPR TC 3 GIRPR Workshop, ANNPR 2012. Proceedings. Lecture Notes in Artificial Intelligence. no.7477 Springer Berlin Heidelberg, pp. 1-13.
    PUB | PDF | DOI
     
  • [261]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625238
    Hofmann, D., Gisbrecht, A., & Hammer, B., 2012. Efficient Approximations of Kernel Robust Soft LVQ. In WSOM.
    PUB
     
  • [260]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625271
    Bouveyron, C., Hammer, B., & Villmann, T., 2012. Recent developments in clustering algorithms. In M. Verleysen, ed. ESANN 2012. pp. 447-458.
    PUB
     
  • [259]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625276
    Gisbrecht, A., Mokbel, B., & Hammer, B., 2012. Linear Basis-Function t-SNE for Fast Nonlinear Dimensionality Reduction. In IJCNN.
    PUB
     
  • [258]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622453
    Hammer, B., Gisbrecht, A., & Schulz, A., 2012. How to Visualize Large Data Sets? Presented at the Workshop Advances in Self-Organizing Maps (WSOM), Santiago, Chile.
    PUB | DOI
     
  • [257]
    2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2625223
    Hammer, B., 2012. Challenges in Neural Computation. Künstliche Intelligenz : KI, 26(4), p 333-340.
    PUB | DOI
     
  • [256]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625242
    Gross, S., et al., 2012. Feedback Provision Strategies in Intelligent Tutoring Systems Based on Clustered Solution Spaces. In DeLFI. pp. 27-38.
    PUB
     
  • [255]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625247
    Gisbrecht, A., Hofmann, D., & Hammer, B., 2012. Discriminative Dimensionality Reduction Mappings. In J. Hollmén, F. Klawonn, & A. Tucker, eds. Advances in Intelligent Data Analysis XI - 11th International Symposium, IDA 2012, Helsinki, Finland, October 25-27, 2012. Proceedings. Lecture Notes in Computer Science. no.7619 Springer, pp. 126-138.
    PUB
     
  • [254]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625254
    Hofmann, D., & Hammer, B., 2012. Kernel Robust Soft Learning Vector Quantization. In N. Mana, F. Schwenker, & E. Trentin, eds. Artificial Neural Networks in Pattern Recognition - 5th INNS IAPR TC 3 GIRPR Workshop, ANNPR 2012, Trento, Italy, September 17-19, 2012. Proceedings. Lecture Notes in Computer Science. no.7477 Springer, pp. 14-23.
    PUB
     
  • [253]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615750
    Schleif, F.-M., et al., 2012. Fast approximated relational and kernel clustering. In Proceedings of ICPR 2012. IEEE, pp. 1229-1232.
    PUB
     
  • [252]
    2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2671281
    Hammer, B., & Villmann, T., 2012. Special issue on new challenges in neural computation 2012. Neurocomputing, 131: 1.
    PUB | DOI | WoS
     
  • [251]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2536437 OA
    Gross, S., et al., 2012. Cluster based feedback provision strategies in intelligent tutoring systems. In Proceedings of the 11th international conference on Intelligent Tutoring Systems. Berlin, Heidelberg: Springer-Verlag, pp. 699-700.
    PUB | PDF | DOI | Download (ext.)
     
  • [250]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2536444 OA
    Gross, S., et al., 2012. Feedback Provision Strategies in Intelligent Tutoring Systems Based on Clustered Solution Spaces. In J. Desel, et al., eds. DeLFI 2012: Die 10. e-Learning Fachtagung Informatik. GI-Edition : Proceedings. no.207 Hagen, Germany: Köllen, pp. 27-38.
    PUB | PDF
     
  • [249]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615756
    Schleif, F.-M., Zhu, X., & Hammer, B., 2012. Soft Competitive Learning for large data sets. In Proceedings of MCSD 2012. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 141-151.
    PUB | DOI
     
  • [248]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534877
    Schleif, F.-M., et al., 2012. Learning Relevant Time Points for Time-Series Data in the Life Sciences. In ICANN (2). Lecture Notes in Computer Science. no.7553 Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 531-539.
    PUB | DOI
     
  • [247]
    2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2489405
    Bunte, K., et al., 2012. Limited Rank Matrix Learning, discriminative dimension reduction and visualization. Neural Networks, 26, p 159-173.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [246]
    2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2474292
    Bunte, K., Biehl, M., & Hammer, B., 2012. A General Framework for Dimensionality-Reducing Data Visualization Mapping. Neural Computation, 24(3), p 771-804.
    PUB | DOI | WoS
     
  • [245]
    2012 | Konferenzbeitrag | PUB-ID: 2909356
    Mokbel, B., et al., 2012. Visualizing the quality of dimensionality reduction. In M. Verleysen, ed. ESANN 2012. pp. 179--184.
    PUB
     
  • [244]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534888
    Schleif, F.-M., Zhu, X., & Hammer, B., 2012. A Conformal Classifier for Dissimilarity Data. In AIAI (2). Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 234-243.
    PUB | DOI
     
  • [243]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534910
    Zhu, X., Schleif, F.-M., & Hammer, B., 2012. Patch Processing for Relational Learning Vector Quantization. In ISNN (1). Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 55-63.
    PUB | DOI
     
  • [242]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534868
    Hammer, B., et al., 2012. White Box Classification of Dissimilarity Data. In HAIS (1). Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 309-321.
    PUB | DOI | WoS
     
  • [241]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534905
    Schleif, F.-M., Gisbrecht, A., & Hammer, B., 2012. Relevance learning for short high-dimensional time series in the life sciences. In IEEE Computational Intelligence Society & Institute of Electrical and Electronics Engineers, eds. IJCNN. Piscataway, NJ: IEEE, pp. 1-8.
    PUB | DOI
     
  • [240]
    2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2509852
    Zhu, X., et al., 2012. Approximation techniques for clustering dissimilarity data. Neurocomputing, 90, p 72-84.
    PUB | DOI | WoS
     
  • [239]
    2011 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982113
    Hammer, B., et al., 2011. Topographic Mapping of Dissimilarity Data. In J. Laaksonen & T. Honkela, eds. Advances in Self-Organizing Maps. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 1-15.
    PUB | DOI
     
  • [238]
    2011 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982112
    Hammer, B., Schleif, F.-M., & Zhu, X., 2011. Relational Extensions of Learning Vector Quantization. In B. - L. Lu, L. Zhang, & J. Kwok, eds. Neural Information Processing. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 481-489.
    PUB | DOI
     
  • [237]
    2011 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982111
    Hammer, B., et al., 2011. Prototype-Based Classification of Dissimilarity Data. In J. Gama, E. Bradley, & J. Hollmén, eds. Advances in Intelligent Data Analysis X. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 185-197.
    PUB | DOI
     
  • [236]
    2011 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982110
    Schleif, F.-M., Gisbrecht, A., & Hammer, B., 2011. Accelerating Kernel Neural Gas. In T. Honkela, et al., eds. Artificial Neural Networks and Machine Learning – ICANN 2011. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 150-158.
    PUB | DOI
     
  • [235]
    2011 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982109
    Hammer, B., et al., 2011. A General Framework for Dimensionality Reduction for Large Data Sets. In J. Laaksonen & T. Honkela, eds. Advances in Self-Organizing Maps. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 277-287.
    PUB | DOI
     
  • [234]
    2011 | Preprint | Veröffentlicht | PUB-ID: 2534994
    Schleif, F.-M., Gisbrecht, A., & Hammer, B., 2011. Supervised learning of short and high-dimensional temporal sequences for life science measurements.
    PUB | arXiv
     
  • [233]
    2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276480
    Gisbrecht, A., et al., 2011. Linear time heuristics for topographic mapping of dissimilarity data. In Intelligent Data Engineering and Automated Learning - IDEAL 2011: IDEAL 2011, 12th international conference, Norwich, UK, September 7 - 9, 2011 ; proceedings. Lecture Notes in Computer Science. no.6936 Berlin, Heidelberg: Springer, pp. 25-33.
    PUB | DOI
     
  • [232]
    2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276485
    Hammer, B., et al., 2011. Topographic Mapping of Dissimilarity Data. In WSOM'11.
    PUB
     
  • [231]
    2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276492
    Schleif, F.-M., Gisbrecht, A., & Hammer, B., 2011. Accelerating Kernel Neural Gas. In S. Kaski, et al., eds. ICANN'2011.
    PUB
     
  • [230]
    2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276500
    Kaestner, M., et al., 2011. Generalized Functional Relevance Learning Vector Quantization. In M. Verleysen, ed. European Symposium on Artificial Neural Networks. D side, pp. pp. 93-98.
    PUB
     
  • [229]
    2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276512
    Hammer, B., et al., 2011. A general framework for dimensionality reduction for large data sets. In WSOM'11.
    PUB
     
  • [228]
    2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276517
    Bunte, K., Biehl, M., & Hammer, B., 2011. Supervised dimension reduction mappings. In M. Verleysen, ed. European Symposium on Artificial Neural Networks. D side, pp. pp. 281-286.
    PUB
     
  • [227]
    2011 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2276531
    Gisbrecht, A., Mokbel, B., & Hammer, B., 2011. Relational Generative Topographic Mapping. Neurocomputing, 74(9), p 1359-1371.
    PUB | DOI | WoS
     
  • [226]
    2011 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2276506
    Bunte, K., et al., 2011. Neighbor embedding XOM for dimension reduction and visualization. Neurocomputing, 74(9), p 1340-1350.
    PUB | DOI | WoS
     
  • [225]
    2011 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2309980
    Schleif, F.-M., et al., 2011. Efficient Kernelized Prototype-based Classification. International Journal of Neural Systems, 21(06), p 443-457.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [224]
    2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276522
    Gisbrecht, A., et al., 2011. Accelerating dissimilarity clustering for biomedical data analysis. In IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. pp. pp.154-161.
    PUB
     
  • [223]
    2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276527
    Bunte, K., Biehl, M., & Hammer, B., 2011. Dimensionality Reduction Mappings. In IEEE Computational Intelligence Society, ed. IEEE Symposium on Computational Intelligence and Data Mining. Piscataway, NJ: IEEE, pp. pp. 349-356.
    PUB | DOI
     
  • [222]
    2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2091665
    Zhu, X., & Hammer, B., 2011. Patch Affinity Propagation. Presented at the 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium.
    PUB
     
  • [221]
    2011 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993288
    Arnonkijpanich, B., Hasenfuss, A., & Hammer, B., 2011. Local matrix adaptation in topographic neural maps. Neurocomputing, 74(4), p 522-539.
    PUB | DOI | WoS
     
  • [220]
    2011 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2276540
    Gisbrecht, A., & Hammer, B., 2011. Relevance learning in generative topographic mapping. Neurocomputing, 74(9), p 1351-1358.
    PUB | DOI | WoS
     
  • [219]
    2010 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982117
    Gisbrecht, A., et al., 2010. Visualizing Dissimilarity Data Using Generative Topographic Mapping. In R. Dillmann, et al., eds. KI 2010: Advances in Artificial Intelligence. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 227-237.
    PUB | DOI
     
  • [218]
    2010 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982116
    Villmann, T., et al., 2010. The Mathematics of Divergence Based Online Learning in Vector Quantization. In F. Schwenker & N. El Gayar, eds. Artificial Neural Networks in Pattern Recognition. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 108-119.
    PUB | DOI
     
  • [217]
    2010 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982115
    Arnonkijpanich, B., & Hammer, B., 2010. Global Coordination Based on Matrix Neural Gas for Dynamic Texture Synthesis. In F. Schwenker & N. El Gayar, eds. Artificial Neural Networks in Pattern Recognition. 4th IAPR TC3 Workshop, ANNPR 2010, Cairo, Egypt, April 11-13, 2010. Proceedings. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 84-95.
    PUB | DOI
     
  • [216]
    2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2982114
    Haupt, A., et al., 2010. Automated generation of classifier based monitoring functions and its application to automotive steering control. IFAC Proceedings Volumes, 43(7), p 721-726.
    PUB | DOI
     
  • [215]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276543
    Gisbrecht, A., Mokbel, B., & Hammer, B., 2010. The Nystrom approximation for relational generative topographic mappings. In NIPS workshop on challenges of Data Visualization.
    PUB
     
  • [214]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994127
    Villmann, T., et al., 2010. Divergence Based Online Learning in Vector Quantization. In L. Rutkowski, et al., eds. Artificial Intelligence and Soft Computing. Lecture Notes in Computer Science, 6113. Berlin, Heidelberg: Springer, pp. 479-486.
    PUB | DOI
     
  • [213]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1796018
    Arnonkijpanich, B., Hasenfuss, A., & Hammer, B., 2010. Local matrix learning in clustering and applications for manifold visualization. Neural Networks, 23(4), p 476-486.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [212]
    2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1796195
    Schneider, P., Biehl, M., & Hammer, B., 2010. Hyperparameter learning in probabilistic prototype-based models. Neurocomputing, 73(7-9), p 1117-1124.
    PUB | DOI | WoS
     
  • [211]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993273
    Arnonkijpanich, B., & Hammer, B., 2010. Global Coordination based on Matrix Neural Gas for Dynamic Texture Synthesis. In N. El Gayar & F. Schwenker, eds. ANNPR'2010. Lecture Notes in Artificial Intelligence, 5998. Springer, pp. 84-95.
    PUB
     
  • [210]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993367
    Bunte, K., et al., 2010. Exploratory Observation Machine (XOM) with Kullback-Leibler Divergence for Dimensionality Reduction and Visualization. In M. Verleysen, ed. ESANN'10. Proceedings of the 18th European Symposium on Artificial Neural Networks. Evere: D side, pp. 87-92.
    PUB
     
  • [209]
    2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1929672
    Witoelar, A.W., et al., 2010. Window-Based Example Selection in Learning Vector Quantization. Neural Computing, 22(11), p 2924-2961.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [208]
    2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1796189
    Bunte, K., et al., 2010. Adaptive local dissimilarity measures for discriminative dimension reduction of labeled data. Neurocomputing, 73(7-9), p 1074-1092.
    PUB | DOI | WoS
     
  • [207]
    2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1794373
    Hammer, B., & Hasenfuss, A., 2010. Topographic Mapping of Large Dissimilarity Data Sets. Neural Computation, 22(9), p 2229-2284.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [206]
    2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1795962
    Schneider, P., et al., 2010. Regularization in Matrix Relevance Learning. IEEE Transactions on Neural Networks, 21(5), p 831-840.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [205]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993978
    Schleif, F.-M., et al., 2010. Generalized derivative based Kernelized learning vector quantization. In C. Fyfe, et al., eds. Intelligent Data Engineering and Automated Learning – IDEAL 2010 11th International Conference, Paisley, UK, September 1-3, 2010. Proceedings. Berlin u.a.: Springer, pp. 21-28.
    PUB | DOI
     
  • [204]
    2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994034
    Simmuteit, S., et al., 2010. Evolving trees for the retrieval of mass spectrometry-based bacteria fingerprints. Knowledge and Information Systems, 25(2), p 327-343.
    PUB | DOI | WoS
     
  • [203]
    2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993435
    Geweniger, T., et al., 2010. Median fuzzy-c-means for clustering dissimilarity data. Neurocomputing, 73(7-9), p 1109-1116.
    PUB | DOI | WoS
     
  • [202]
    2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993466
    Gori, M., et al., 2010. Perspectives and challenges for recurrent neural network training. Logic Journal of the IGPL, 18(5), p 617-619.
    PUB | DOI | WoS
     
  • [201]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993536
    Hammer, B., & Hasenfuss, A., 2010. Clustering very large dissimilarity data sets. In F. Schwenker & N. El Gayar, eds. Artificial Neural Networks in Pattern Recognition (ANNPR 2010). Proceedings. Lecture Notes in Artificial Intelligence. no.5998 Berlin: Springer, pp. 259-273.
    PUB | DOI
     
  • [200]
    2010 | Konferenzband | Veröffentlicht | PUB-ID: 2276535
    B. Hammer, et al., eds., 2010. Learning paradigms in dynamic environments, 25.07.10-30.07.20, no.10302, Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany.
    PUB
     
  • [199]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276547
    Mokbel, B., Gisbrecht, A., & Hammer, B., 2010. On the effect of clustering on quality assessment measures for dimensionality reduction. In NIPS workshop on Challenges of Data Visualization.
    PUB
     
  • [198]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993448
    Gisbrecht, A., & Hammer, B., 2010. Relevance learning in generative topographic maps. In M. Verleysen, ed. ESANN'10. Evere: D side, pp. 387-392.
    PUB
     
  • [197]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993452
    Gisbrecht, A., Mokbel, B., & Hammer, B., 2010. Relational Generative Topographic Map. In M. Verleysen, ed. ESANN'10. Evere: D side, pp. 277-282.
    PUB
     
  • [196]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993457
    Gisbrecht, A., et al., 2010. Visualizing Dissimilarity Data using generative topographic mapping. In R. Dillmann, et al., eds. KI'2010. pp. 227-237.
    PUB
     
  • [195]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994138
    Villmann, T., et al., 2010. The Mathematics of Divergence Based Online Learning in Vector Quanitzation. In N. El Gayar & F. Schwenker, eds. ANNPR'2010. Berlin, Heidelberg: Springer, pp. 108-119.
    PUB
     
  • [194]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994227
    Villmann, T., Schleif, F.-M., & Hammer, B., 2010. Sparse representation of data. In M. Verleysen, ed. ESANN'10. D side, pp. 225-234.
    PUB
     
  • [193]
    2009 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982118
    Villmann, T., & Hammer, B., 2009. Functional Principal Component Learning Using Oja’s Method and Sobolev Norms. In J. C. Príncipe & R. Miikkulainen, eds. Advances in Self-Organizing Maps. 7th International Workshop, WSOM 2009, St. Augustine, FL, USA, June 8-10, 2009. Proceedings. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 325-333.
    PUB | DOI
     
  • [192]
    2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993679
    Hammer, B., Schrauwen, B., & Steil, J.J., 2009. Recent advances in efficient learning of recurrent networks. In M. Verleysen, ed. European Symposium on Artificial Neural Networks. Brugge: d-facto, pp. 213-226.
    PUB
     
  • [191]
    2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993984
    Schleif, F.-M., et al., 2009. Cancer Informatics by Prototype-networks in Mass Spectrometry. Artificial Intelligence in Medicine, 45(2-3), p 215-228.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [190]
    2009 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1994160
    Villmann, T., Hammer, B., & Biehl, M., 2009. Some theoretical aspects of the neural gas vector quantizer. In M. Biehl, et al., eds. Similarity Based Clustering. Lecture Notes Artificial Intelligence, 5400. Berlin, Heidelberg: Springer, pp. 23-34.
    PUB | DOI
     
  • [189]
    2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994305
    Witolaer, A., Biehl, M., & Hammer, B., 2009. Equilibrium properties of offline LVQ. In M. Verleysen, ed. European Symposium on Artificial Neural Networks. d-side publications, pp. 535-540.
    PUB
     
  • [188]
    2009 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1993326
    Biehl, M., et al., 2009. Metric learning for prototype based classification. In M. Bianchini, M. Maggini, & F. Scarselli, eds. Innovations in Neural Information – Paradigms and Applications. Studies in Computational Intelligence, 247. Berlin: Springer, pp. 183-199.
    PUB | DOI
     
  • [187]
    2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993422
    Geweniger, T., et al., 2009. Fuzzy variant of affinity propagation in comparison to median fuzzy c-means. In J. C. Principe & R. Miikkulainen, eds. Advances in Self-Organizing Maps. pp. 72-79.
    PUB | DOI
     
  • [186]
    2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993994
    Schneider, P., Biehl, M., & Hammer, B., 2009. Hyperparameter Learning in robust soft LVQ. In M. Verleysen, ed. European Symposium on Artificial Neural Networks. d-side publications, pp. 517-522.
    PUB
     
  • [185]
    2009 | Konferenzband | Veröffentlicht | PUB-ID: 1994310
    M. Biehl, et al., eds., 2009. Similarity-based learning on structures, no.9081, Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany.
    PUB
     
  • [184]
    2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994008
    Schneider, P., Biehl, M., & Hammer, B., 2009. Distance learning in discriminative vector quantization. Neural Computation, 21(10), p 2942-2969.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [183]
    2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993269
    Alex, N., Hasenfuss, A., & Hammer, B., 2009. Patch Clustering for Massive Data Sets. Neurocomputing, 72(7-9), p 1455-1469.
    PUB | DOI | WoS
     
  • [182]
    2009 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1993555
    Hammer, B., Hasenfuss, A., & Rossi, F., 2009. Median topographic maps for biological data sets. In M. Biehl, et al., eds. Similarity Based Clustering. Lecture Notes Artificial Intelligence, 5400. Berlin, Heidelberg: Springer, pp. 92-117.
    PUB | DOI
     
  • [181]
    2009 | Report | Veröffentlicht | PUB-ID: 1993316
    Biehl, M., et al., 2009. Stationarity of Matrix Relevance Learning Vector Quantization, Machine Learning Reports, Leipzig: Universität Leipzig.
    PUB
     
  • [180]
    2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993361
    Bunte, K., Hammer, B., & Biehl, M., 2009. Nonlinear dimension reduction and visualization of labeled data. In X. Jiang & N. Petkov, eds. International Conference on Computer Analysis of Images and Patterns. Lecture Notes in Computer Science, 5702. no.5702 Berlin: Springer, pp. 1162-1170.
    PUB | DOI
     
  • [179]
    2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993429
    Geweniger, T., et al., 2009. Median variant of fuzzy-c-means. In M. Verleysen, ed. European Symposium on Artificial Neural Networks. Evere: d-side publications, pp. 523-528.
    PUB
     
  • [178]
    2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993835
    Mokbel, B., Hasenfuss, A., & Hammer, B., 2009. Graph-based Representation of Symbolic Musical Data. In A. Torsello, et al., eds. Graph-Based Representation in Pattern Recognition (GbRPR 2009). Lecture Notes in Computer Science, 5534. Lecture notes in computer science. no.5534 Berlin: Springer, pp. 42-51.
    PUB | DOI
     
  • [177]
    2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994004
    Schneider, P., Biehl, M., & Hammer, B., 2009. Adaptive relevance matrices in learning vector quantization. Neural Computation, 21(12), p 3532-3561.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [176]
    2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993356
    Bunte, K., Biehl, M., & Hammer, B., 2009. Nonlinear discriminative data visualization. In M. Verleysen, ed. European Symposium on Artificial Neural Networks. Evere: d-side publications, pp. 65-70.
    PUB
     
  • [175]
    2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994152
    Villmann, T., & Hammer, B., 2009. Functional principal component learning using Oja's method and Sobolev norms. In J. C. Principe & R. Miikkulainen, eds. Advances in Self-Organizing Maps. pp. 325-333.
    PUB
     
  • [174]
    2009 | Herausgeber*in Sammelwerk | Veröffentlicht | PUB-ID: 1994316
    M. Biehl, et al., eds., 2009. Similarity Based Clustering, Springer Lecture Notes Artificial Intelligence, 5400, Berlin, Heidelberg: Springer.
    PUB | DOI
     
  • [173]
    2008 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982119
    Arnonkijpanich, B., et al., 2008. Matrix Learning for Topographic Neural Maps. In V. Kůrková, R. Neruda, & J. Koutník, eds. Artificial Neural Networks - ICANN 2008. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 572-582.
    PUB | DOI
     
  • [172]
    2008 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1993939
    Schleif, F.-M., Villmann, T., & Hammer, B., 2008. Pattern Recognition by Supervised Relevance Neural Gas and its Application to Spectral Data in Bioinformatics. In J. R. -n R. -al Dopico, J. Dorado, & A. Pazos, eds. Encyclopedia of Artificial Intelligence. IGI Global, pp. 1337-1342.
    PUB
     
  • [171]
    2008 | Konferenzband | Veröffentlicht | PUB-ID: 1994329
    L. de Raedt, et al., eds., 2008. Recurrent Neural Networks - Models, Capacities, and Applications, no.8041, Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI).
    PUB
     
  • [170]
    2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993282
    Arnonkijpanich, B., et al., 2008. Matrix Learning for Topographic Neural Maps. In V. Kurková, R. Neruda, & J. Koutn'ık, eds. ICANN (1). Lecture Notes in Computer Science, 5163. Berlin: Springer, pp. 572-582.
    PUB
     
  • [169]
    2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993261
    Alex, N., & Hammer, B., 2008. Parallelizing single pass patch clustering. In M. Verleysen, ed. European Symposium on Artificial Neural Networks. Evere, Belgium: d-side publications, pp. 227-232.
    PUB
     
  • [168]
    2008 | Report | Veröffentlicht | PUB-ID: 1993278
    Arnonkijpanich, B., Hammer, B., & Hasenfuss, A., 2008. Local Matrix Adaptation in Topographic Neural Maps, IfI-08-07, Clausthal-Zellerfeld: Clausthal University of Technology.
    PUB
     
  • [167]
    2008 | Report | Veröffentlicht | PUB-ID: 1993379
    Bunte, K., et al., 2008. Discriminative Visualization by Limited Rank Matrix Learning, Machine Learning Reports, Leipzig: Universität Leipzig.
    PUB
     
  • [166]
    2008 | Report | Veröffentlicht | PUB-ID: 1994012
    Schneider, P., Biehl, M., & Hammer, B., 2008. Matrix Adaptation in Discriminative Vector Quantization, IfI Technical Report Seriess, Clausthal-Zellerfeld: Clausthal University of Technology.
    PUB
     
  • [165]
    2008 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994290
    Witoelar, A., et al., 2008. Learning dynamics and robustness of vector quantization and neural gas. Neurocomputing, 71(7-9), p 1210-1219.
    PUB | DOI | WoS
     
  • [164]
    2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993776
    Hasenfuss, A., Boerger, W., & Hammer, B., 2008. Topographic processing of very large text datasets. In C. H. Daglie, ed. Smart Systems Engineering: Computational Intelligence in Architecting Systes (ANNIE 2008). ASME Press, pp. 525-532.
    PUB | DOI
     
  • [163]
    2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993788
    Hasenfuss, A., & Hammer, B., 2008. Single Pass Clustering and Classification of Large Dissimilarity Datasets. In B. Prasad, et al., eds. Artificial Intelligence and Pattern Recognition. ISRST, pp. 219-223.
    PUB
     
  • [162]
    2008 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993966
    Schleif, F.-M., Villmann, T., & Hammer, B., 2008. Prototype based Fuzzy Classification in Clinical Proteomics. International Journal of Approximate Reasoning, 47(1), p 4-16.
    PUB | DOI | WoS
     
  • [161]
    2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994072
    Strickert, M., et al., 2008. Discriminatory Data Mapping by Matrix-Based Supervised Learning Metrics. In L. Prevost, S. Marinai, & F. Schwenker, eds. Artificial Neural Networks in Pattern Recognition. Third IAPR Workshop. Proceedings. Lecture Notes in Computer Science, 5064. Berlin: Springer, pp. 78-89.
    PUB | DOI
     
  • [160]
    2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994089
    Strickert, M., et al., 2008. Robust Centroid-Based Clustering using Derivatives of Pearson Correlation. In P. Encarnação & A. Veloso, eds. BIOSIGNALS (2). INSTICC - Institute for Systems and Technologies of Information, Control and Communication, pp. 197-203.
    PUB
     
  • [159]
    2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994281
    Winkler, T., et al., 2008. Thinning Mesh Animations. In O. Deussen, D. Keim, & D. Saupe, eds. Proceedings of Vision, Modeling, and Visualization 2008. Konstanz, Germany: Aka, pp. 149-158.
    PUB
     
  • [158]
    2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993804
    Hasenfuss, A., Hammer, B., & Rossi, F., 2008. Patch Relational Neural Gas - Clustering of Huge Dissimilarity Datasets. In L. Prevost, S. Marinai, & F. Schwenker, eds. Artificial Neural Networks in Pattern Recognition, Third IAPR Workshop. Proceedings. Lecture Notes in Computer Science, 5064. Berlin: Springer, pp. 1-12.
    PUB | DOI
     
  • [157]
    2008 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1993900
    Schleif, F.-M., Hammer, B., & Villmann, T., 2008. Analysis of Spectral Data in Clinical Proteomics by use of Learning Vector Quantizers. In M. Van de Werff, A. Delder, & R. Tollenaar, eds. Computational Intelligence in Biomedicine and Bioinformatics: Current Trends and Applications. Berlin: Springer, pp. 141-167.
    PUB | DOI
     
  • [156]
    2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993798
    Hasenfuss, A., et al., 2008. Magnification Control in Relational Neural Gas. In M. Verleysen, ed. European Symposium on Artificial Neural Networks. Brussels: d-side publications, pp. 325-330.
    PUB
     
  • [155]
    2008 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994253
    Villmann, T., et al., 2008. Classification of mass-spectrometric data in clinical proteomics using learning vector quantization methods. Briefings in Bioinformatics, 9(2), p 129-143.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [154]
    2008 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2017617
    Villmann, T., et al., 2008. Fuzzy Classification Using Information Theoretic Learning Vector Quantization. Neurocomputing, 71(16-18), p 3070-3076.
    PUB | DOI | WoS
     
  • [153]
    2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2001836
    Geweniger, T., et al., 2008. Comparison of cluster algorithms for the analysis of text data using Kolmogorov complexity. In M. Köppen, N. K. Kasabov, & G. G. Coghill, eds. ICONIP 2008. Berlin, Heidelberg: Springer, pp. 61-69.
    PUB | DOI
     
  • [152]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993848 OA
    Rossi, F., Hasenfuß, A., & Hammer, B., 2007. Accelerating Relational Clustering Algorithms With Sparse Prototype Representation. In Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007). Bielefeld: Bielefeld University.
    PUB | PDF | DOI
     
  • [151]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994016 OA
    Schneider, P., et al., 2007. Advanced metric adaptation in Generalized LVQ for classification of mass spectrometry data. In Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007). Bielefeld: Bielefeld University.
    PUB | PDF | DOI
     
  • [150]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994267 OA
    Villmann, T., et al., 2007. Class imaging of hyperspectral satellite remote sensing data using FLSOM. In Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007). Bielefeld: Bielefeld University.
    PUB | PDF | DOI
     
  • [149]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994295 OA
    Witoelar, A., Biehl, M., & Hammer, B., 2007. Learning Vector Quantization: generalization ability and dynamics of competing prototypes. In Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007). Bielefeld: Bielefeld University.
    PUB | PDF | DOI
     
  • [148]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993265 OA
    Alex, N., Hammer, B., & Klawonn, F., 2007. Single pass clustering for large data sets. In Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007). Bielefeld: Bielefeld University.
    PUB | PDF | DOI
     
  • [147]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993563 OA
    Hammer, B., et al., 2007. Topographic Processing of Relational Data. In Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007). Bielefeld: Bielefeld University.
    PUB | PDF | DOI
     
  • [146]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993547
    Hammer, B., et al., 2007. Intuitive Clustering of Biological Data. In Proceedings of International Joint Conference on Neural Networks. IEEE, pp. 1877-1882.
    PUB | DOI
     
  • [145]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993782
    Hasenfuss, A., & Hammer, B., 2007. Relational topographic maps. In M. R. Berthold, J. Shawe-Taylor, & N. Lavrac, eds. Advances in Intelligent Data Analysis VII, Proceedings of the 7th International Symposium on Intelligent Data Analysis. no.4723 Berlin: Springer, pp. 93-105.
    PUB | DOI
     
  • [144]
    2007 | Report | Veröffentlicht | PUB-ID: 1993922
    Schleif, F.-M., Hasenfuss, A., & Hammer, B., 2007. Aggregation of multiple peak lists by use of an improved neural gas network, Leipzig: Universität Leipzig.
    PUB
     
  • [143]
    2007 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993297
    Biehl, M., Ghosh, A., & Hammer, B., 2007. Dynamics and generalization ability of LVQ algorithms. Journal of Machine Learning Research, 8, p 323-360.
    PUB
     
  • [142]
    2007 | Report | Veröffentlicht | PUB-ID: 1993533
    Hammer, B., & Hasenfuss, A., 2007. Relational topographic Maps, IfI Technical reports, Clausthal-Zellerfeld: Clausthal University of Technology.
    PUB
     
  • [141]
    2007 | Report | Veröffentlicht | PUB-ID: 1993831
    Melato, M., Hammer, B., & Hormann, K., 2007. Neural Gas for Surface Reconstruction, IfI Technical reports, Clausthal-Zellerfeld: Clausthal University of Technology.
    PUB
     
  • [140]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993970
    Schleif, F.-M., Villmann, T., & Hammer, B., 2007. Analysis of Proteomic Spectral Data by Multi Resolution Analysis and Self-Organizing-Maps. In F. Masulli, S. Mitra, & G. Pasi, eds. Application of Fuzzy Sets Theory. Proceedings of the 7th International Workshop on Fuzzy Logic and Applications. LNAI 4578. Berlin, Heidelberg: Springer, pp. 563-570.
    PUB | DOI
     
  • [139]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993999
    Schneider, P., Biehl, M., & Hammer, B., 2007. Relevance matrices in LVQ. In M. Verleysen, ed. Proc. Of European Symposium on Artificial Neural Networks. Brussels, Belgium: d-side publications, pp. 37-42.
    PUB
     
  • [138]
    2007 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993911
    Schleif, F.-M., Hammer, B., & Villmann, T., 2007. Margin based Active Learning for LVQ Networks. Neurocomputing, 70(7-9), p 1215-1224.
    PUB | DOI | WoS
     
  • [137]
    2007 | Report | Veröffentlicht | PUB-ID: 1993334
    Blazewicz, J., Ecker, K., & Hammer, B., 2007. ICOLE-2007, German-Polish Workshop on Computational Biology, Scheduling and Machine Learning. Lessach, Austria, 27.05.-02.06.2007, Clausthal-Zellerfeld: Clausthal University of Technology.
    PUB
     
  • [136]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993746
    Hammer, B., & Villmann, T., 2007. How to process uncertainty in machine learning. In M. Verleysen, ed. Proc. Of European Symposium on Artificial Neural Networks (ESANN'2007). Brussels, Belgium: d-side publications, pp. 79-90.
    PUB
     
  • [135]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993811
    Hasenfuss, A., et al., 2007. Neural gas clustering for dissimilarity data with continuous prototypes. In F. Sandoval, et al., eds. Computational and Ambient Intelligence – Proceedings of the 9th Work-conference on Artificial Neural Networks. LNCS 4507. Berlin: Springer, pp. 539-546.
    PUB | DOI
     
  • [134]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994299
    Witolaer, A., et al., 2007. On the dynamics of vector quantization and neural gas. In M. Verleysen, ed. Proc. Of European Symposium on Artificial Neural Networks (ESANN'2007). Brussels, Belgium: d-side publications, pp. 127-132.
    PUB
     
  • [133]
    2007 | Konferenzband | Veröffentlicht | PUB-ID: 1994321
    M. Biehl, et al., eds., 2007. Similarity-based Clustering and its Application to Medicine and Biology, no.7131, Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI).
    PUB
     
  • [132]
    2007 | Herausgeber*in Sammelwerk | Veröffentlicht | PUB-ID: 1994326
    B. Hammer & P. Hitzler, eds., 2007. Perspectives of Neural-Symbolic Integration, Studies in Computational Intelligence, 77, Berlin: Springer.
    PUB | DOI
     
  • [131]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993541
    Hammer, B., & Hasenfuss, A., 2007. Relational Neural Gas. In J. Hertzberg, M. Beetz, & R. Englert, eds. KI 2007: Advances in Artificial Intelligence. Lecture Notes in Artificial Intelligence, 4667. Berlin: Springer, pp. 190-204.
    PUB | DOI
     
  • [130]
    2007 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993616
    Hammer, B., Hasenfuss, A., & Villmann, T., 2007. Magnification control for batch neural gas. Neurocomputing, 70(7-9), p 1225-1234.
    PUB | DOI | WoS
     
  • [129]
    2007 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1993630
    Hammer, B., Micheli, A., & Sperduti, A., 2007. Adaptive Contextual Processing of Structured Data by Recursive Neural Networks: A Survey of Computational Properties. In B. Hammer & P. Hitzler, eds. Perspectives of Neural-Symbolic Integration. Studies in computational Intelligence, 77. Berlin: Springer, pp. 67-94.
    PUB | DOI
     
  • [128]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993820
    Hasenfuss, A., et al., 2007. Neural gas clustering for sparse proximity data. In F. Sandoval, et al., eds. Proceedings of the 9th International Work-Conference on Artificial Neural Networks.LNCS 4507. Berlin, Heidelberg, Germany: Springer, pp. 539-546.
    PUB
     
  • [127]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993907
    Schleif, F.-M., Hammer, B., & Villmann, T., 2007. Supervised Neural Gas for Functional Data and its Application to the Analysis of Clinical Proteom Spectra. In F. Sandoval, et al., eds. Computational and Ambient Intelligence. Proceedings of the 9th International Work-Conference on Artificial Neural Networks. LNCS, 4507. Berlin, Heidelberg: Springer, pp. 1036-1044.
    PUB | DOI
     
  • [126]
    2007 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1994102
    Tino, P., Hammer, B., & Boden, M., 2007. Markovian Bias of Neural-based Architectures With Feedback Connections. In B. Hammer & P. Hitzler, eds. Perspectives of Neural-Symbolic Integration. Studies in computational Intelligence, 77. Berlin: Springer, pp. 95-134.
    PUB | DOI
     
  • [125]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994258
    Villmann, T., et al., 2007. Fuzzy Labeled Self Organizing Map for Clasification of Spectra. In F. Sandoval, et al., eds. Computational and Ambient Intelligence. Proceedings of the 9th Work-conference on Artificial Neural Networks. LNCS, 4507. Berlin: Springer, pp. 556-563.
    PUB | DOI
     
  • [124]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993578
    Hammer, B., et al., 2006. Supervised Batch Neural Gas. In F. Schwenker, ed. Proceedings of Conference Artificial Neural Networks in Pattern Recognition (ANNPR). Berlin: Springer Verlag, pp. 33-45.
    PUB | DOI
     
  • [123]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993895
    Schleif, F.-M., Hammer, B., & Villmann, T., 2006. Margin based Active Learning for LVQ Networks. In M. Verleysen, ed. Proc. Of European Symposium on Artificial Neural Networks. Brussels, Belgium: d-side publications, pp. 539-544.
    PUB
     
  • [122]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994184
    Villmann, T., et al., 2006. Prototype based classification using information theoretic learning. In I. King, et al., eds. Neural Information Processing, 13th International Conference. Proceedings. Lecture Notes in Computer Science, 4233. no.Part II Berlin: Springer, pp. 40-49.
    PUB
     
  • [121]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994273
    Villmann, T., et al., 2006. Fuzzy Labeled Self-Organizing Map with Label-Adjusted Prototypes. In F. Schwenker, ed. Proceedings of Conference Artificial Neural Networks in Pattern Recognition. Berlin: Springer, pp. 46-56.
    PUB | DOI
     
  • [120]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993889
    Schleif, F.-M., et al., 2006. Machine Learning and Soft-Computing in Bioinformatics. A Short Journey. In Proc. of FLINS 2006. World Scientific Press, pp. 541-548.
    PUB
     
  • [119]
    2006 | Report | Veröffentlicht | PUB-ID: 1993322
    Biehl, M., Hammer, B., & Schneider, P., 2006. Matrix Learning in Learning Vector Quantization, Clausthal-Zellerfeld: Clausthal University of Technology.
    PUB
     
  • [118]
    2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993391
    Cottrell, M., et al., 2006. Batch and Median Neural Gas. Neural Networks, 19(6-7), p 762-771.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [117]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993568
    Hammer, B., et al., 2006. Supervised median neural gas. In C. Dagli, et al., eds. Smart Engineering System Design. Intelligent Engineering Systems Through Artificial Neural Networks, 16. ASME Press, pp. 623-633.
    PUB
     
  • [116]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993594
    Hammer, B., et al., 2006. Supervised median clustering. In C. H. Dagli, ed. 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). ASME Press series on intelligent engineering systems through artificial neural networks, 16. New York, NY: ASME Press, pp. 623-632.
    PUB
     
  • [115]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993878
    Schleif, F.-M., et al., 2006. Analysis and Visualization of Proteomic Data by Fuzzy labeled Self-Organizing Maps. In D. J. Lee, et al., eds. 19th IEEE International Symposium on Computer- based Medical Systems. Los Alamitos: IEEE Computer Society Press, pp. 919-924.
    PUB | DOI
     
  • [114]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994028
    Seiffert, U., et al., 2006. Neural Networks and Machine Learning in Bioinformatics - Theory and Applications. In M. Verleysen, ed. Proc. Of European Symposium on Artificial Neural Networks. Brussels, Belgium: d-side publications, pp. 521-532.
    PUB
     
  • [113]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994201
    Villmann, T., Hammer, B., & Seiffert, U., 2006. Perspectives of Self-adapted Self-organizing Clustering in Organic Computing. In A. J. Ijspeert, T. Masuzawa, & S. Kusumoto, eds. Biologically Inspired Approaches to Advanced Information Technology, Second International Workshop. Proceedings. Lecture Notes in Computer Science, 3853. Berlin: Springer, pp. 141-159.
    PUB | DOI
     
  • [112]
    2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994237
    Villmann, T., Schleif, F.-M., & Hammer, B., 2006. Comparison of relevance learning vector quantization with other metric adaptive classification methods. Neural Networks, 19(5), p 610-622.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [111]
    2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993301
    Biehl, M., Ghosh, A., & Hammer, B., 2006. Learning vector quantization: The dynamics of winner-takes-all algorithms. Neurocomputing, 69(7-9), p 660-670.
    PUB | DOI | WoS
     
  • [110]
    2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993440
    Ghosh, A., Biehl, M., & Hammer, B., 2006. Performance analysis of LVQ algorithms: a statistical physics approach. Neural Networks, 19(6-7), p 817-829.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [109]
    2006 | Report | Veröffentlicht | PUB-ID: 1993584
    Hammer, B., et al., 2006. Supervised median clustering, IfI Technical reports, Clausthal-Zellerfeld: Clausthal University of Technology.
    PUB
     
  • [108]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993611
    Hammer, B., Hasenfuss, A., & Villmann, T., 2006. Magnification Control for Batch Neural Gas. In M. Verleysen, ed. Proc. Of European Symposium on Artificial Neural Networks. Brussels: d-side publications, pp. 7-12.
    PUB
     
  • [107]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993659
    Hammer, B., & Neubauer, N., 2006. On the capacity of unsupervised recursive neural networks for symbol processing. In A. d'Avila Garcez, P. Hitzler, & G. Tamburrini, eds. Workshop proceedings of NeSy'06.
    PUB
     
  • [106]
    2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993762
    Hammer, B., & Villmann, T., 2006. Effizient Klassifizieren und Clustern: Lernparadigmen von Vektorquantisierern. Künstliche Intelligenz, 3(6), p 5-11.
    PUB
     
  • [105]
    2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994082
    Strickert, M., et al., 2006. Generalized relevance LVQ (GRLVQ) with correlation measures for gene expression analysis. Neurocomputing, 69(7-9), p 651-659.
    PUB | DOI | WoS
     
  • [104]
    2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994195
    Villmann, T., et al., 2006. Fuzzy Classification by Fuzzy Labeled Neural Gas. Neural Networks, 19(6-7), p 772-779.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [103]
    2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994241
    Villmann, T., Schleif, F.-M., & Hammer, B., 2006. Prototype-based fuzzy classification with local relevance for proteomics. Neurocomputing, 69(16-18), p 2425-2428.
    PUB | DOI | WoS
     
  • [102]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2017225
    Hammer, B., et al., 2006. Learning vector quantization classification with local relevance determination for medical data. In L. Rutkowski, et al., eds. Artificial Intelligence and Soft-Computing - Proceedings of ICAISC 2006. LNAI, 4029. Lecture notes in computer science ; 4029 : Lecture notes in artificial intelligence. no.4029 Berlin, Heidelberg: Springer, pp. 603-612.
    PUB | DOI
     
  • [101]
    2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982120
    Villmann, T., Schleif, F.-M., & Hammer, B., 2005. Fuzzy Labeled Soft Nearest Neighbor Classification with Relevance Learning. In Fourth International Conference on Machine Learning and Applications (ICMLA'05). IEEE, pp. 11-15.
    PUB | DOI
     
  • [100]
    2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993624
    Hammer, B., et al., 2005. Self Organizing Maps for Time Series. In Proceedings of WSOM 2005. pp. 115-122.
    PUB
     
  • [99]
    2005 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994057
    Strickert, M., & Hammer, B., 2005. Merge SOM for temporal data. Neurocomputing, 64, p 39-71.
    PUB | DOI | WoS
     
  • [98]
    2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994172
    Villmann, T., et al., 2005. Fuzzy Labeled Neural GAS for Fuzzy Classification. In M. Cottrell, ed. Proceedings of the 5th Workshop on Self-Organizing Maps [on CD-ROM]. Paris, France: University Paris-1-Pantheon-Sorbonne, pp. 283-290.
    PUB
     
  • [97]
    2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993305
    Biehl, M., Gosh, A., & Hammer, B., 2005. The dynamics of Learning Vector Quantization. In M. Verleysen, ed. ESANN'05. Evere: d-side publishing, pp. 13-18.
    PUB
     
  • [96]
    2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993386
    Cottrell, M., et al., 2005. Batch NG. In Proceedings of WSOM 2005. pp. 275-282.
    PUB
     
  • [95]
    2005 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993406
    DasGupta, B., & Hammer, B., 2005. On approximate learning by multi-layered feedforward circuits. Theoretical Computer Science, 348(1), p 95-127.
    PUB | DOI | WoS
     
  • [94]
    2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993444
    Ghosh, A., Biehl, M., & Hammer, B., 2005. Dynamical Analysis of LVQ type learning rules. In Proceedings of WSOM. pp. 578-594.
    PUB
     
  • [93]
    2005 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993641
    Hammer, B., Micheli, A., & Sperduti, A., 2005. Universal approximation capability of cascade correlation for structures. Neural Computation, 17(5), p 1109-1159.
    PUB | DOI | WoS
     
  • [92]
    2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993665
    Hammer, B., et al., 2005. Relevance learning for mental disease classification. In M. Verleysen, ed. ESANN'05. d-side publishing, pp. 139-144.
    PUB
     
  • [91]
    2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994118
    Tluk von Toschanowitz, K., Hammer, B., & Ritter, H., 2005. Relevance determination in reinforcement learning. In M. Verleysen, ed. ESANN'05. d-side publishing, pp. 369-374.
    PUB
     
  • [90]
    2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994219
    Villmann, T., Schleif, F.-M., & Hammer, B., 2005. Fuzzy Classification for Classification of Mass Spectrometric Data Based on Learning Vector Quantization. In International Workshop on Integrative Bioinformatics.
    PUB
     
  • [89]
    2005 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993396
    Cottrell, M., Hammer, B., & Villmann, T., 2005. New Aspects in Neurocomputing. Neurocomputing, 63, p 1-3.
    PUB | DOI | WoS
     
  • [88]
    2005 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993416
    Gersmann, K., & Hammer, B., 2005. Improving iterative repair strategies for scheduling with the SVM. Neurocomputing, 63, p 271-292.
    PUB | DOI | WoS
     
  • [87]
    2005 | Report | Veröffentlicht | PUB-ID: 1993675
    Hammer, B., Schleif, F.-M., & Villmann, T., 2005. On the Generalization Ability of Prototype-Based Classifiers with Local Relevance Determination, IfI Technical reports, Clausthal-Zellerfeld: Clausthal University of Technology.
    PUB
     
  • [86]
    2005 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993721
    Hammer, B., Strickert, M., & Villmann, T., 2005. Supervised neural gas with general similarity measure. Neural Processing Letters, 21(1), p 21-44.
    PUB | DOI | WoS
     
  • [85]
    2005 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993671
    Hammer, B., Saunders, C., & Sperduti, A., 2005. Special issue on neural networks and kernel methods for structured domains. Neural Networks, 18(8), p 1015-1018.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [84]
    2005 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1993710
    Hammer, B., Strickert, M., & Villmann, T., 2005. Prototype based recognition of splice sites. In U. Seiffert, L. C. Jain, & P. Schweitzer, eds. Bioinformatics using computational intelligence paradigms. Berlin: Springer, pp. 25-55.
    PUB
     
  • [83]
    2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993974
    Schleif, F.-M., Villmann, T., & Hammer, B., 2005. Local Metric Adaptation for Soft Nearest Prototype Classification to Classify Proteomic Data. In I. Bloch, A. Petrosino, & A. G. B. Tettamanzi, eds. Proceedings of the 6th Workshop on Fuzzy Logic and Applications. Berlin, Heidelberg: Springer, pp. 290-296.
    PUB | DOI
     
  • [82]
    2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994249
    Villmann, T., Schleif, F.-M., & Hammer, B., 2005. Fuzzy labeled soft nearest neighbor classification with relevance learning. In M. A. Wani, K. J. Cios, & K. Hafeez, eds. Proceedings of the International Conference of Machine Learning Applications. Los Angeles: IEEE Press, pp. 11-15.
    PUB
     
  • [81]
    2005 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993717
    Hammer, B., Strickert, M., & Villmann, T., 2005. On the generalization ability of GRLVQ networks. Neural Processing Letters, 21(2), p 109-120.
    PUB | DOI | WoS
     
  • [80]
    2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993750
    Hammer, B., & Villmann, T., 2005. Classification using non standard metrics. In M. Verleysen, ed. ESANN'05. Brussels: d-side publishing, pp. 303-316.
    PUB
     
  • [79]
    2005 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994063
    Strickert, M., Hammer, B., & Blohm, S., 2005. Unsupervised recursive sequences processing. Neurocomputing, 63, p 69-97.
    PUB | DOI | WoS
     
  • [78]
    2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982121
    Gersmann, K., & Hammer, B., 2004. A reinforcement learning algorithm to improve scheduling search heuristics with the SVM. In 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541). no.3 IEEE, pp. 1811-1816.
    PUB | DOI
     
  • [77]
    2004 | Report | Veröffentlicht | PUB-ID: 1993732
    Hammer, B., Tino, P., & Micheli, A., 2004. A mathematical characterization of the architectural bias of recursive models, Osnabrücker Schriften zur Mathematik, Osnabrück: Universität Osnabrück.
    PUB
     
  • [76]
    2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994168
    Villmann, T., Hammer, B., & Schleif, F.-M., 2004. Metrik Adaptation for Optimal Feature Classification in Learning Vector Quantization Applied to Environment Detection. In H. - M. Groß, K. Debes, & H. - J. Böhme, eds. Proceedings of Selbstorganisation Von Adaptivem Verfahren. Fortschritts-Berichte VDI Reihe 10, Nr. 742. VDI Verlag, pp. 592-597.
    PUB
     
  • [75]
    2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994111
    Tluk von Toschanowitz, K., Hammer, B., & Ritter, H., 2004. Mapping the Design Space of Reinforcement Learning Problems - a Case Study. In H. - M. Gross, K. Debes, & H. - J. Böhme, eds. SOAVE 2004, 3rd Workshop on SelfOrganization of AdaptiVE Behavior. VDI Verlag, pp. 251-261.
    PUB
     
  • [74]
    2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994212
    Villmann, T., Schleif, F.-M., & Hammer, B., 2004. Metric adaptation for optimal feature classification in learning vector quantization applied to environment detection. In H. - M. Groß, K. Debes, & H. - J. Böhme, eds. SOAVE 2004, 3rd Workshop on SelfOrganization of AdaptiVE Behavior. VDI Verlag.
    PUB
     
  • [73]
    2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993620
    Hammer, B., & Jain, B.J., 2004. Neural methods for non-standard data. In M. Verleysen, ed. European Symposium on Artificial Neural Networks'2004. D-side publications, pp. 281-292.
    PUB
     
  • [72]
    2004 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993649
    Hammer, B., et al., 2004. Recursive self-organizing network models. Neural Networks, 17(8-9), p 1061-1085.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [71]
    2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993702
    Hammer, B., Strickert, M., & Villmann, T., 2004. Relevance LVQ versus SVM. In L. Rutkowski, et al., eds. Artificial Intelligence and Softcomputing, Lecture Notes in Artificial Intelligence, 3070. Berlin: Springer, pp. 592-597.
    PUB
     
  • [70]
    2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993419
    Gersmann, K., & Hammer, B., 2004. A reinforcement learning algorithm to improve scheduling search heuristics with the SVM. In IJCNN.
    PUB
     
  • [69]
    2004 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993654
    Hammer, B., et al., 2004. A general framework for unsupervised processing of structured data. Neurocomputing, 57, p 3-35.
    PUB | DOI | WoS
     
  • [68]
    2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993870
    Schleif, F.-M., et al., 2004. Supervised Relevance Neural Gas and Unified Maximum Separability Analysis for Classification of Mass Spectrometric Data. In M. A. Wani, K. J. Cios, & K. Hafeez, eds. Proceedings of the 3rd International Conference on Machine Learning and Applications (ICMLA) 2004. Los Alamitos, CA, USA: IEEE Press, pp. 374-379.
    PUB
     
  • [67]
    2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994049
    Strickert, M., & Hammer, B., 2004. Self-organizing context learning. In M. Verleysen, ed. European Symposium on Artificial Neural Networks. D-side publications, pp. 39-44.
    PUB
     
  • [66]
    2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994099
    Tino, P., & Hammer, B., 2004. On early stages of learning in connectionist models with feedback connections. In Compositional Connectionism in Cognitive Science.
    PUB
     
  • [65]
    2003 | Preprint | PUB-ID: 2999891 OA
    Hammer, B., 2003. Mathematical Aspects of Neural Netorks (Habilitation). Habilitationsschrift (kumulativ).
    PUB | PDF
     
  • [64]
    2003 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2982124
    Hammer, B., & Tiňo, P., 2003. Recurrent Neural Networks with Small Weights Implement Definite Memory Machines. Neural Computation, 15(8), p 1897-1929.
    PUB | DOI
     
  • [63]
    2003 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2982123
    Villmann, T., Merényi, E., & Hammer, B., 2003. Neural maps in remote sensing image analysis. Neural Networks, 16(3-4), p 389-403.
    PUB | DOI
     
  • [62]
    2003 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2982122
    Tiňo, P., & Hammer, B., 2003. Architectural Bias in Recurrent Neural Networks: Fractal Analysis. Neural Computation, 15(8), p 1931-1957.
    PUB | DOI
     
  • [61]
    2003 | Report | Veröffentlicht | PUB-ID: 1993725
    Hammer, B., Strickert, M., & Villmann, T., 2003. On the generalization ability of GRLVQ, Osnabrücker Schriften zur Mathematik, Osnabrück: Universität Osnabrück.
    PUB
     
  • [60]
    2003 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994108
    Tiño, P., & Hammer, B., 2003. Architectural Bias in Recurrent Neural Networks: Fractal Analysis. Neural Computation, 15(8), p 1931-1957.
    PUB
     
  • [59]
    2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994223
    Villmann, T., Schleif, F.-M., & Hammer, B., 2003. Supervised Neural Gas and Relevance Learning in Learning Vector Quantization. In T. Yamakawa, ed. Proceedings of the 4th Workshop on Self Organizing Maps [on CD-ROM]. Hibikino, Kitakyushu, Japan: Kyushu Institute of Technology, pp. 47-52.
    PUB
     
  • [58]
    2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993338
    Bojer, T., Hammer, B., & Koeers, C., 2003. Monitoring technical systems with prototype based clustering. In M. Verleysen, ed. ESANN 2003, 10th European Symposium on Artificial Neural Network. Proceedings. Evere: D-side publication, pp. 433-439.
    PUB
     
  • [57]
    2003 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993530
    Hammer, B., & Gersmann, K., 2003. A Note on the Universal Approximation Capability of Support Vector Machines. Neural Processing Letters, 17(1), p 43-53.
    PUB
     
  • [56]
    2003 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1993487
    Hammer, B., 2003. Perspectives on learning symbolic data with connectionistic systems. In R. Kühn, et al., eds. Adaptivity and Learning. Berlin: Springer, pp. 141-160.
    PUB
     
  • [55]
    2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993754
    Hammer, B., & Villmann, T., 2003. Mathematical Aspects of Neural Networks. In M. Verleysen, ed. Proc. Of European Symposium on Artificial Neural Networks (ESANN'2003). Brussels, Belgium: d-side, pp. 59-72.
    PUB
     
  • [54]
    2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994053
    Strickert, M., & Hammer, B., 2003. Unsupervised recursive sequence processing. In M. Verleysen, ed. 10th European Symposium on Artificial Neural Networks. Proceedings. D-side publication, pp. 27-32.
    PUB
     
  • [53]
    2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994060
    Strickert, M., & Hammer, B., 2003. Neural Gas for Sequences. In WSOM'03. pp. 53-57.
    PUB
     
  • [52]
    2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993412
    Gersmann, K., & Hammer, B., 2003. Improving iterative repair strategies for scheduling with the SVM. In M. Verleysen, ed. ESANN 2003, 10th European Symposium on Artificial Neural Networks. Proceedings. Evere: D-side publication, pp. 235-240.
    PUB
     
  • [51]
    2003 | Report | Veröffentlicht | PUB-ID: 1993645
    Hammer, B., Micheli, a., & Sperduti, A., 2003. A general framework for self-organizing structure processing neural networks, Pisa: Universita di Pisa, Dipartimento die Informatica.
    PUB
     
  • [50]
    2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993349
    Bojer, T., et al., 2003. Determining Relevant Input Dimensions for the Self-Organizing Map. In L. Rutkowski & J. Kacprzyk, eds. Neural Networks and Soft Computing (Proc. ICNNSC 2002). Physica-Verlag, pp. 388-393.
    PUB
     
  • [49]
    2003 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993736
    Hammer, B., & Tiño, P., 2003. Recurrent Neural Networks with Small Weights Implement Definite Memory Machines. Neural Computation, 15(8), p 1897-1929.
    PUB
     
  • [48]
    2003 | Report | Veröffentlicht | PUB-ID: 1994157
    Villmann, T., & Hammer, B., 2003. Metric adaptation and relevance learning in learning vector quantization, Osnabrücker Schriften zur Mathematik, Osnabrück: Universität Osnabrück.
    PUB
     
  • [47]
    2003 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994208
    Villmann, T., Merényi, E., & Hammer, B., 2003. Neural maps in remote sensing image analysis. Neural Networks, 16(3-4), p 389-403.
    PUB
     
  • [46]
    2002 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2982126
    Hammer, B., 2002. Recurrent networks for structured data – A unifying approach and its properties. Cognitive Systems Research, 3(2), p 145-165.
    PUB | DOI
     
  • [45]
    2002 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2982125
    Hammer, B., & Villmann, T., 2002. Generalized relevance learning vector quantization. Neural Networks, 15(8-9), p 1059-1068.
    PUB | DOI
     
  • [44]
    2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993636
    Hammer, B., Micheli, A., & Sperduti, A., 2002. A general framework for unsupervised processing of structured data. In M. Verleysen, ed. ESANN 2002, 10th European Symposium on Artificial Neural Network. Proceedings. De-side publication, pp. 389-394.
    PUB
     
  • [43]
    2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994095
    Tino, P., & Hammer, B., 2002. Architectural bias in recurrent neural networks – fractal analysis. In J. R. Dorronsoro, ed. Proc. International Conf. on Artificial Neural Networks. Lecture Notes in Computer Science, 2415. Berlin: Springer, pp. 370-376.
    PUB
     
  • [42]
    2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994146
    Villmann, T., & Hammer, B., 2002. Supervised Neural Gas for Learning Vector Quantization. In D. Polani, J. Kim, & T. Martinetz, eds. Proc. of the 5th German Workshop on Artificial Life. Berlin: Akademische Verlagsgesellschaft - infix - IOS Press, pp. 9-16.
    PUB
     
  • [41]
    2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993688
    Hammer, B., & Steil, J.J., 2002. Perspectives on Learning with Recurrent Neural Networks. In M. Verleysen, ed. Proc. European Symposium Artificial Neural Networks. D-side publication, pp. 357-368.
    PUB
     
  • [40]
    2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993758
    Hammer, B., & Villmann, T., 2002. Batch-GRLVQ. In M. Verleysen, ed. Proc. Of European Symposium on Artificial Neural Networks (ESANN'2002). Brussels, Belgium: d-side, pp. 295-300.
    PUB
     
  • [39]
    2002 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993765
    Hammer, B., & Villmann, T., 2002. Generalized Relevance Learning Vector Quantization. Neural Networks, 15(8-9), p 1059-1068.
    PUB
     
  • [38]
    2002 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1993471
    Hammer, B., 2002. Compositionality in Neural Systems. In M. Arbib, ed. Handbook of Brain Theory and Neural Networks. 2nd. MIT Press, pp. 244-248.
    PUB
     
  • [37]
    2002 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993508
    Hammer, B., 2002. Recurrent neural networks for structured data – a unifying approach and its properties. Cognitive Systems Research, 3(2), p 145-165.
    PUB
     
  • [36]
    2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993692
    Hammer, B., Strickert, M., & Villmann, T., 2002. Learning Vector Quantization for Multimodal Data. In J. R. Dorronsoro, ed. Proc. International Conf. on Artificial Neural Networks (ICANN). Lecture Notes in Computer Science, 2415. Berlin: Springer Verlag, pp. 370-376.
    PUB
     
  • [35]
    2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993697
    Hammer, B., Strickert, M., & Villmann, T., 2002. Rule Extraction from Self-Organizing Networks. In J. R. Dorronsoro, ed. Proc. International Conf. on Artificial Neural Networks (ICANN). Lecture Notes in Computer Science, 2415. Berlin: Springer Verlag, pp. 877-883.
    PUB
     
  • [34]
    2002 | Report | Veröffentlicht | PUB-ID: 1993729
    Hammer, B., & Tino, P., 2002. Neural networks with small weights implement finite memory machines, Osnabrücker Schriften zur Mathematik, Osnabrück: Universität Osnabrück.
    PUB
     
  • [33]
    2001 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982130
    Hammer, B., 2001. On the Generalization Ability of Recurrent Networks. In G. Dorffner, H. Bischof, & K. Hornik, eds. Artificial Neural Networks — ICANN 2001. Lecture Notes in Computer Science. no. 2130 Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 731-736.
    PUB | DOI
     
  • [32]
    2001 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982129
    Strickert, M., Bojer, T., & Hammer, B., 2001. Generalized Relevance LVQ for Time Series. In G. Dorffner, H. Bischof, & K. Hornik, eds. Artificial Neural Networks — ICANN 2001. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 677-683.
    PUB | DOI
     
  • [31]
    2001 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2982128
    Hammer, B., 2001. Generalization ability of folding networks. IEEE Transactions on Knowledge and Data Engineering, 13(2), p 196-206.
    PUB | DOI
     
  • [30]
    2001 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2982127
    Vidyasagar, M., Balaji, S., & Hammer, B., 2001. Closure properties of uniform convergence of empirical means and PAC learnability under a family of probability measures. Systems & Control Letters, 42(2), p 151-157.
    PUB | DOI
     
  • [29]
    2001 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993768
    Hammer, B., & Villmann, T., 2001. Input Pruning for Neural Gas Architectures. In Proc. Of European Symposium on Artificial Neural Networks (ESANN'2001). Brussels, Belgium: D facto publications, pp. 283-288.
    PUB
     
  • [28]
    2001 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993343
    Bojer, T., et al., 2001. Relevance determination in learning vector quantization. In M. Verleysen, ed. ESANN'2001. D-facto publications, pp. 271-276.
    PUB
     
  • [27]
    2001 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994123
    Vidyasagar, M., Balaji, S., & Hammer, B., 2001. Closure properties of uniform convergence of empirical means and PAC learnability under a family of probability measures. System and Control Letters, 42, p 151-157.
    PUB
     
  • [26]
    2001 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993474
    Hammer, B., 2001. On the Generalization Ability of Recurrent Networks. In G. Dorffner, H. Bischof, & K. Hornik, eds. Artificial Neural Networks. Proceedings. Lecture Notes in Computer Science, 2130. Berlin: Springer, pp. 731-736.
    PUB
     
  • [25]
    2001 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993739
    Hammer, B., & Villmann, T., 2001. Estimating Relevant Input Dimensions for Self-Organizing Algorithms. In N. M. Allinson, et al., eds. Advances in Self-Organising Maps. London: Springer, pp. 173-180.
    PUB
     
  • [24]
    2001 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994042
    Strickert, M., Bojer, T., & Hammer, B., 2001. Generalized Relevance LVQ for Time Series. In G. Dorffner, H. Bischof, & K. Hornik, eds. Artificial Neural Networks. International Conference. Proceedings. Lecture Notes in Computer Science, 2130. Berlin: Springer, pp. 677-683.
    PUB
     
  • [23]
    2001 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993510
    Hammer, B., 2001. Generalization Ability of Folding Networks. IEEE Trans. Knowl. Data Eng., 13(2), p 196-206.
    PUB
     
  • [22]
    2000 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2982131
    Hammer, B., 2000. On the approximation capability of recurrent neural networks. Neurocomputing, 31(1-4), p 107-123.
    PUB | DOI
     
  • [21]
    2000 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993499
    Hammer, B., 2000. Limitations of hybrid systems. In M. Verleysen, ed. European Symposium on Artificial Neural Networks. D-facto publications, pp. 213-218.
    PUB
     
  • [20]
    2000 | Monographie | Veröffentlicht | PUB-ID: 1993514
    Hammer, B., 2000. Learning with Recurrent Neural Networks, Lecture Notes in Control and Information Sciences, 254, Berlin: Springer.
    PUB
     
  • [19]
    2000 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993512
    Hammer, B., 2000. On the approximation capability of recurrent neural networks. Neurocomputing, 31(1-4), p 107-123.
    PUB
     
  • [18]
    2000 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993400
    DasGupta, B., & Hammer, B., 2000. On Approximate Learning by Multi-layered Feedforward Circuits. In H. Arimura, S. Jain, & A. Sharma, eds. Algorithmic Learning Theory, 11th International Conference. Proceedings. Lecture Notes in Computer Science, 1968. Berlin: Springer, pp. 264-278.
    PUB
     
  • [17]
    2000 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993479
    Hammer, B., 2000. Approximation and generalization issues of recurrent networks dealing with structured data. In P. Frasconi, A. Sperduti, & M. Gori, eds. ECAI workshop: Foundations of connectionist-symbolic integration: representation, paradigms, and algorithms.
    PUB
     
  • [16]
    2000 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993495
    Hammer, B., 2000. Neural networks classifying symbolic data. In L. de Raedt & S. Kramer, eds. ICML workshop on attribute-value and relational learning: crossing the boundaries. pp. 61-65.
    PUB
     
  • [15]
    1999 | Dissertation | PUB-ID: 2999889 OA
    Hammer, B., 1999. Learning with Recurrent Neural Networks (PhD thesis), Universität Osnabrück.
    PUB | PDF
     
  • [14]
    1999 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2982132
    Hammer, B., 1999. On the Learnability of Recursive Data. Mathematics of Control, Signals, and Systems, 12(1), p 62-79.
    PUB | DOI
     
  • [13]
    1999 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993516
    Hammer, B., 1999. On the learnability of recursive data. Mathematics of Control, Signals and Systems, 12, p 62-79.
    PUB
     
  • [12]
    1999 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993502
    Hammer, B., 1999. Approximation capabilities of folding networks. In M. Verleysen, ed. European Symposium on Artificial Neural Networks. D-facto publications, pp. 33-38.
    PUB
     
  • [11]
    1999 | Report | Veröffentlicht | PUB-ID: 1993409
    DasGupta, B., & Hammer, B., 1999. Hardness of approximation of the loading problem for multi-layered feedforward neural networks, DIMACS Center, Rutgers University.
    PUB
     
  • [10]
    1998 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993484
    Hammer, B., 1998. On the Approximation Capability of Recurrent Neural Networks. In M. Heiss, ed. Proceedings of the International ICSC / IFAC Symposium on Neural Computation (NC 1998). ICSC Academic Press, pp. 512-518.
    PUB
     
  • [9]
    1998 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993505
    Hammer, B., 1998. Training a sigmoidal network is difficult. In M. Verleysen, ed. European Symposium on Artificial Neural Networks. D-facto publications, pp. 255-260.
    PUB
     
  • [8]
    1998 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993518
    Hammer, B., 1998. Some complexity results for perceptron networks. In International Conference on artificial Neural Networks. pp. 639-644.
    PUB
     
  • [7]
    1997 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993526
    Hammer, B., 1997. Generalization of Elman Networks. In Artificial Neural Networks - ICANN '97, 7th International Conference. Proceedings. Lecture Notes in Computer Science, 1327. Berlin: Springer, pp. 409-414.
    PUB
     
  • [6]
    1997 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993684
    Hammer, B., & Sperschneider, V., 1997. Neural networks can approximate mappings on structured objects. In P. P. Wang, ed. International conference on Computational Intelligence and Neural Networks. pp. 211-214.
    PUB
     
  • [5]
    1997 | Report | Veröffentlicht | PUB-ID: 1993524
    Hammer, B., 1997. On the generalization ability of simple recurrent neural networks, Osnabrücker Schriften zur Mathematik, Osnabrück: Universität Osnabrück.
    PUB
     
  • [4]
    1997 | Report | Veröffentlicht | PUB-ID: 1993520
    Hammer, B., 1997. Learning recursive data is intractable, Osnabrücker Schriften zur Mathematik, Osnabrück: Universität Osnabrück.
    PUB
     
  • [3]
    1997 | Report | Veröffentlicht | PUB-ID: 1993522
    Hammer, B., 1997. A NP-hardness result for a sigmoidal 3-node neural network, Osnabrücker Schriften zur Mathematik, Osnabrück: Universität Osnabrück.
    PUB
     
  • [2]
    1996 | Report | Veröffentlicht | PUB-ID: 1993528
    Hammer, B., 1996. Universal approximation of mappings on structured objects using the folding architecture, Osnabrücker Schriften zur Mathematik, Osnabrück: Universität Osnabrück.
    PUB
     
  • [1]
    1996 | Monographie | Veröffentlicht | PUB-ID: 1994039
    Sperschneider, V., & Hammer, B., 1996. Theoretische Informatik. Eine problemorientierte Einführung, erlin: Springer.
    PUB
     
Markierte Publikation(en)

Suche

Publikationen filtern

Darstellung / Sortierung

Export / Einbettung