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. Intelligent Data Engineering and Automated Learning – IDEAL 2024, PT I, Lecture Notes in Computer Science, 15346, 301-312. Cham: Springer . https://doi.org/10.1007/978-3-031-77731-8_28
    PUB | DOI | WoS
     
  • [550]
    2025 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 3001658
    Peters, H., Mazur, A., Pandey, A. K., Trächtler, A., Hammer, B., & Homberg, W. (2025). Development of a digital twin for data-driven modeling of punch-bending processes using a graphical modeling notation. at - Automatisierungstechnik, 73(3), 173-184. https://doi.org/10.1515/auto-2024-0112
    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. 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). https://doi.org/10.1109/IJCNN60899.2024.10651287
    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. 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). https://doi.org/10.1109/IJCNN60899.2024.10651496
    PUB | DOI | WoS
     
  • [547]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 3001626
    Internò, C., Olhofer, M., Jin, Y., & Hammer, B. (2024). Federated Loss Exploration for Improved Convergence on Non-IID Data. 2024 International Joint Conference on Neural Networks (IJCNN), IEEE International Joint Conference on Neural Networks (IJCNN) New York: Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/IJCNN60899.2024.10651455
    PUB | DOI | WoS
     
  • [546]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2993740
    Vaquet, V., Hinder, F., Vaquet, J., Lammers, K., Quakernack, L., & Hammer, B. (2024). Localizing of Anomalies in Critical Infrastructure using Model-Based Drift Explanations. 2024 International Joint Conference on Neural Networks (IJCNN), 1-8. IEEE. https://doi.org/10.1109/IJCNN60899.2024.10651472
    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. 2024 International Joint Conference on Neural Networks (IJCNN), 1-8. IEEE. https://doi.org/10.1109/IJCNN60899.2024.10651275
    PUB | DOI | WoS
     
  • [544]
    2024 | Konferenzbeitrag | PUB-ID: 3000176
    Fumagalli, F., Muschalik, M., Kolpaczki, P., Hüllermeier, E., & Hammer, B. (2024). KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions. In R. Salakhutdinov, Z. Kolter, K. Heller, A. Weller, N. Oliver, J. Scarlett, & F. Berkenkamp (Eds.), Proceedings of Machine Learning Research: Vol. 235. Proceedings of the 41st International Conference on Machine Learning (pp. 14308-14342). PMLR.
    PUB
     
  • [543]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2993714 OA
    Mazur, A., Roberts, J. (I. ), Leins, D., Schulz, A., & Hammer, B. (2024). Visualizing and Improving 3D Mesh Segmentation with DeepView. ESANN 2024 proceedings, 649-654. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com. https://doi.org/10.14428/esann/2024.ES2024-135
    PUB | PDF | DOI | Download (ext.)
     
  • [542]
    2024 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2993739
    Vaquet, V., Hinder, F., Artelt, A., Ashraf, I., Strotherm, J., Vaquet, J., Brinkrolf, J., et al. (2024). Challenges, Methods, Data–A Survey of Machine Learning in Water Distribution Networks. In M. Wand, K. Malinovská, J. Schmidhuber, & I. V. Tetko (Eds.), Lecture Notes in Computer Science. Artificial Neural Networks and Machine Learning – ICANN 2024. 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17–20, 2024, Proceedings, Part IX (pp. 155-170). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-72356-8_11
    PUB | DOI | WoS
     
  • [541]
    2024 | Preprint | PUB-ID: 2999454
    Velioglu, R., Bevandic, P., Chan, R., & Hammer, B. (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., Grimmelsmann, N., Schneider, A., & Hammer, B. (2024). Similarity-Based Zero-Shot Domain Adaptation for Wearables. ESANN 2024 proceedings, 221-226. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com. https://doi.org/10.14428/esann/2024.ES2024-123
    PUB | DOI
     
  • [539]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2994158
    Hammer, B. (2024). Explaining Neural Networks - Deep and Shallow. In T. Villmann, M. Kaden, T. Geweniger, & F. - M. Schleif (Eds.), Lecture Notes in Networks and Systems: Vol. 1087. Advances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and Beyond (WSOM+ 2024) (pp. 139-140). Cham: Springer . https://doi.org/10.1007/978-3-031-67159-3_16
    PUB | DOI | WoS
     
  • [538]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2991394
    Kolpaczki, P., Muschalik, M., Fumagalli, F., Hammer, B., & Hüllermeier, E. (2024). SVARM-IQ: Efficient Approximation of Any-order Shapley Interactions through Stratification. In S. Dasgupta, S. Mandt, & Y. Li (Eds.), Proceedings of Machine Learning Research: Vol. 238. International Conference on Artificial Intelligence and Statistics, 2-4 May 2024, Palau de Congressos, Valencia, Spain (pp. 3520-3528). San Diego: PMLR.
    PUB | WoS | arXiv
     
  • [537]
    2024 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2994227
    Artelt, A., Kyriakou, M. S., Vrachimis, S. G., Eliades, D. G., Hammer, B., & Polycarpou, M. M. (2024). EPyT-Flow: A Toolkit for Generating Water DistributionNetwork Data. Journal of Open Source Software, 9(103), 7104. https://doi.org/10.21105/joss.07104
    PUB | DOI
     
  • [536]
    2024 | Konferenzbeitrag | PUB-ID: 2992095
    Störck, F., Hinder, F., Brinkrolf, J., Paaßen, B., Vaquet, V., & Hammer, B. (2024). FairGLVQ: Fairness in Partition-Based Classification. In T. Villmann, M. Kaden, T. Geweniger, & F. - M. Schleif (Eds.), Proceedings of the 15th International Workshop on Self-Organizing Maps (WSOM 2024) (pp. 141-151). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-67159-3_17
    PUB | DOI | Download (ext.) | WoS
     
  • [535]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2993721
    Peters, H., Djakow, E., Rostek, T., Mazur, A., Trächtler, A., Homberg, W., & Hammer, B. (2024). Novel approach for data-driven modelling of multi-stage straightening and bending processes. Material Forming: ESAFORM 2024, Materials Research Proceedings, 41, 2289-2298. Materials Research Forum LLC. https://doi.org/10.21741/9781644903131-252
    PUB | DOI
     
  • [534]
    2024 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2993652
    Shah, Z. H., Müller, M., Hübner, W., Ortkraß, H., Hammer, B., Huser, T., & Schenck, W. (2024). Image restoration in frequency space using complex-valued CNNs. Frontiers in Artificial Intelligence , 7, 1353873. https://doi.org/10.3389/frai.2024.1353873
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [533]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2993852
    Liuliakov, A., Schulz, A., Hermes, L., & Hammer, B. (2024). Noise Robust One-Class Intrusion Detection on Dynamic Graphs. ESANN 2024 proceedings, 363-368. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com. https://doi.org/10.14428/esann/2024.ES2024-124
    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. https://doi.org/10.3389/frai.2024.1330258
    PUB | PDF | DOI | WoS | PubMed | Europe PMC
     
  • [531]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2993744
    Vaquet, V., Vaquet, J., Hinder, F., Malialis, K., Panayiotou, C., Polycarpou, M., & Hammer, B. (2024). Self-Supervised Learning from Incrementally Drifting Data Streams. ESANN 2024 proceesdings, 431-436. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com. https://doi.org/10.14428/esann/2024.ES2024-49
    PUB | DOI
     
  • [530]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2993743
    Hinder, F., Vaquet, V., & Hammer, B. (2024). On the Fine Structure of Drifting Features. ESANN 2024 proceesdings, 63-68. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com. https://doi.org/10.14428/esann/2024.ES2024-89
    PUB | DOI
     
  • [529]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2993741
    Brinkrolf, J., Vaquet, V., Hinder, F., & Hammer, B. (2024). Causes of Rejects in Prototype-based Classification Aleatoric vs. Epistemic Uncertainty. ESANN 2024 proceesdings, 191-196. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com. https://doi.org/10.14428/esann/2024.ES2024-156
    PUB | DOI
     
  • [528]
    2024 | Report | Veröffentlicht | PUB-ID: 2992602 OA
    Hammer, B., Alaçam, Ö., Arlinghaus, C. S., Brinkmann, M., Dörksen, H., Hoeken, S., Jungeblut, T., et al. (2024). Sustainable Life-Cycle of Intelligent Socio-Technical Systems. https://doi.org/10.4119/unibi/2992602
    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.), Lecture Notes in Computer Science. Advances in Intelligent Data Analysis XXII. 22nd International Symposium on Intelligent Data Analysis, IDA 2024, Stockholm, Sweden, April 24–26, 2024, Proceedings, Part I (pp. 77-89). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-58547-0_7
    PUB | DOI | WoS
     
  • [526]
    2024 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2992700
    Strotherm, J., Müller, A., Hammer, B., & Paaßen, B. (2024). Fairness in KI-Systemen. In S. Schork (Ed.), Vertrauen in Künstliche Intelligenz. Eine multi-perspektivische Betrachtung (pp. 163-183). Wiesbaden: Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-43816-6_9
    PUB | DOI | arXiv
     
  • [525]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2992337 OA
    Zanutto, D., Michalopoulos, C., Chatzistefanou, G. - A., Vamvakeridou-Lyroudia, L., Tsiami, L., Glynis, K., Samartzis, P., et al. (2024). A Water Futures Approach on Water Demand Forecasting with Online Ensemble Learning. The 3rd International Joint Conference on Water Distribution Systems Analysis & Computing and Control for the Water Industry (WDSA/CCWI 2024), 60. Basel Switzerland: MDPI. https://doi.org/10.3390/engproc2024069060
    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. https://doi.org/10.1016/j.neucom.2024.127968
    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. https://doi.org/10.3389/frai.2024.1330257
    PUB | PDF | DOI | WoS | PubMed | Europe PMC
     
  • [522]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2987572
    Schroeder, S., Schulz, A., Hinder, F., & Hammer, B. (2024). Semantic Properties of Cosine Based Bias Scores for Word Embeddings. Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods. Vol. 1, 160-168. Setúbal, Portugal: SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0012577200003654
    PUB | DOI
     
  • [521]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2987573
    Grimmelsmann, N., Mechtenberg, M., Vieth, M., Schulz, A., Hammer, B., & Schneider, A. (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. Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies, 611-621. Setúbal, Portugal: SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0012368700003657
    PUB | DOI
     
  • [520]
    2024 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2988165
    Muschalik, M., Fumagalli, F., Hammer, B., & Hüllermeier, E. (2024). Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles. Proceedings of the AAAI Conference on Artificial Intelligence, 38(13), 14388-14396. https://doi.org/10.1609/aaai.v38i13.29352
    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. Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods ICPRAM. Volume 1, 296-303. SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0012361200003654
    PUB | DOI
     
  • [518]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2984049
    Ashraf, M. I., Hermes, L., Artelt, A., & Hammer, B. (2023). Spatial Graph Convolution Neural Networks for Water Distribution Systems. In B. Crémilleux, S. Hess, & S. Nijssen (Eds.), Lecture Notes in Computer Science. Advances in Intelligent Data Analysis XXI. 21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-la-Neuve, Belgium, April 12–14, 2023, Proceedings (pp. 29-41). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-30047-9_3
    PUB | DOI
     
  • [517]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2983942
    Muschalik, M., Fumagalli, F., Jagtani, R., Hammer, B., & Hüllermeier, E. (2023). iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios. In L. Longo (Ed.), Communications in Computer and Information Science. Explainable Artificial Intelligence. First World Conference, xAI 2023, Lisbon, Portugal, July 26–28, 2023, Proceedings, Part I (pp. 177-194). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-44064-9_11
    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.), Communications in Computer and Information Science. Explainable Artificial Intelligence. First World Conference, xAI 2023, Lisbon, Portugal, July 26–28, 2023, Proceedings, Part III (pp. 280-300). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-44070-0_14
    PUB | DOI | WoS
     
  • [515]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2987580
    Fumagalli, F., Muschalik, M., Kolpaczki, P., Hüllermeier, E., & Hammer, B. (2023). SHAP-IQ: Unified Approximation of any-order Shapley Interactions. 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. https://doi.org/10.1007/s42979-023-01782-5
    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. https://doi.org/10.1016/j.asoc.2022.109942
    PUB | DOI | WoS
     
  • [512]
    2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2979026
    Jakob, J., Artelt, A., Hasenjäger, M., & Hammer, B. (2023). Interpretable SAM-kNN Regressor for Incremental Learning on High-Dimensional Data Streams. Applied Artificial Intelligence, 37(1), 2198846. https://doi.org/10.1080/08839514.2023.2198846
    PUB | DOI | WoS
     
  • [511]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2985684
    Kummert, J., Schulz, A., Feldhans, R., Habigt, M., Stemmler, M., Kohler, C., Abel, D., et al. (2023). Generating Cardiovascular Data to Improve Training of Assistive Heart Devices. 2023 IEEE Symposium Series on Computational Intelligence (SSCI), 1292-1297. IEEE. https://doi.org/10.1109/SSCI52147.2023.10372030
    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. https://doi.org/10.3389/fcomp.2023.1087929
    PUB | PDF | DOI | Download (ext.) | WoS | arXiv
     
  • [509]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2985683
    Feldhans, R., Schulz, A., Kummert, J., Habigt, M., Stemmler, M., Kohler, C., Abel, D., et al. (2023). Data Augmentation for Cardiovascular Time Series Data Using WaveNet. 2023 IEEE Symposium Series on Computational Intelligence (SSCI), 836-841. IEEE. https://doi.org/10.1109/SSCI52147.2023.10371813
    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. https://doi.org/10.1007/978-3-031-43085-5_10
    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. Presented at the
    PUB | PDF
     
  • [506]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2977934
    Hinder, F., Vaquet, V., Brinkrolf, J., & Hammer, B. (2023). On the Change of Decision Boundary and Loss in Learning with Concept Drift. In B. Crémilleux, S. Hess, & S. Nijssen (Eds.), Lecture Notes in Computer Science: Vol. 13876. Advances in Intelligent Data Analysis XXI. 21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-la-Neuve, Belgium, April 12–14, 2023, Proceedings (pp. 182-194). Cham: Springer . https://doi.org/10.1007/978-3-031-30047-9_15
    PUB | DOI
     
  • [505]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982167
    Hinder, F., Vaquet, V., Brinkrolf, J., & Hammer, B. (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 (pp. 164-175). Setúbal: SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0011797500003411
    PUB | DOI
     
  • [504]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969381
    Schroeder, S., Schulz, A., Kenneweg, P., & Hammer, B. (2023). So Can We Use Intrinsic Bias Measures or Not? Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods, 403-410. Setúbal, Portugal: SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0011693700003411
    PUB | DOI
     
  • [503]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969382
    Kenneweg, P., Schroeder, S., Schulz, A., & Hammer, B. (2023). Debiasing Sentence Embedders Through Contrastive Word Pairs. Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods, 205-212. Setúbal, Portugal: SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0011615300003411
    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. Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods, 27-38. Setúbal, Portugal: SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0011618300003411
    PUB | DOI | arXiv
     
  • [501]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2983756
    Fumagalli, F., Muschalik, M., Hüllermeier, E., & Hammer, B. (2023). On Feature Removal for Explainability in Dynamic Environments. ESANN 2023 proceedings, 83-88. https://doi.org/10.14428/esann/2023.ES2023-148
    PUB | DOI
     
  • [500]
    2023 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2983727
    Fumagalli, F., Muschalik, M., Hüllermeier, E., & Hammer, B. (2023). Incremental permutation feature importance (iPFI): towards online explanations on data streams. Machine Learning . https://doi.org/10.1007/s10994-023-06385-y
    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.), Lecture Notes in Computer Science. Advances in Computational Intelligence. 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Ponta Delgada, Portugal, June 19–21, 2023, Proceedings, Part I (pp. 92-104). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-43085-5_8
    PUB | DOI
     
  • [498]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2983455
    Liuliakov, A., Schulz, A., Hermes, L., & Hammer, B. (2023). One-Class Intrusion Detection with Dynamic Graphs. In L. Iliadis, A. Papaleonidas, P. Angelov, & C. Jayne (Eds.), Lecture Notes in Computer Science. 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 (pp. 537-549). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-44216-2_44
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    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2983943
    Muschalik, M., Fumagalli, F., Hammer, B., & Hüllermeier, E. (2023). iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams. In D. Koutra, C. Plant, M. Gomez Rodriguez, E. Baralis, & F. Bonchi (Eds.), Lecture Notes in Computer Science. Machine Learning and Knowledge Discovery in Databases: Research Track. European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part III (pp. 428-445). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-43418-1_26
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    2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2981289
    Hinder, F., Vaquet, V., Brinkrolf, J., & Hammer, B. (2023). Model-based explanations of concept drift. Neurocomputing, 126640. https://doi.org/10.1016/j.neucom.2023.126640
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    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2985571
    Artelt, A., Malialis, K., Panayiotou, C. G., Polycarpou, M. M., & Hammer, B. (2023). Unsupervised Unlearning of Concept Drift with Autoencoders. 2023 IEEE Symposium Series on Computational Intelligence (SSCI), 703-710. IEEE. https://doi.org/10.1109/SSCI52147.2023.10372001
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    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982830
    Hinder, F., & Hammer, B. (2023). Feature Selection for Concept Drift Detection. In M. Verleysen (Ed.), ESANN 2023 Proceedings
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  • [493]
    2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2983759
    Koundouri, P., Hammer, B., Kuhl, U., & Velias, A. (2023). Behavioral Economics and Neuroeconomics of Environmental Values. Annual Review of Resource Economics, 15(1), 153-176. https://doi.org/10.1146/annurev-resource-101722-082743
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  • [492]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2984048
    Schulte-Schüren, C., Wagner, S., Runge, A., Bariamis, D., Hammer, B., Yoneki, E., & Nardi, L. (2023). Best of both, Structured and Unstructured Sparsity in Neural Networks. Proceedings of the 3rd Workshop on Machine Learning and Systems, 104-108. New York, NY, USA: ACM. https://doi.org/10.1145/3578356.3592583
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  • [491]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2984047
    Kenneweg, P., Galli, L., Kenneweg, T., & Hammer, B. (2023). Faster Convergence for Transformer Fine-tuning with Line Search Methods. 2023 International Joint Conference on Neural Networks (IJCNN), 1-8. IEEE. https://doi.org/10.1109/IJCNN54540.2023.10192001
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  • [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. https://doi.org/10.1016/j.neucom.2023.126722
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    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2983457
    Schroeder, S., Schulz, A., Tarakanov, I., Feldhans, R., & Hammer, B. (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.), Lecture Notes in Computer Science. Advances in Computational Intelligence. 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Ponta Delgada, Portugal, June 19–21, 2023, Proceedings, Part I (pp. 134-145). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-43085-5_11
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  • [488]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2983406
    Stahlhofen, P., Artelt, A., Hermes, L., & Hammer, B. (2023). Adversarial Attacks on Leakage Detectors in Water Distribution Networks. In I. Rojas, G. Joya, & A. Catala (Eds.), Lecture Notes in Computer Science. Advances in Computational Intelligence. 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Ponta Delgada, Portugal, June 19–21, 2023, Proceedings, Part II (pp. 451-463). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-43078-7_37
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  • [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. https://doi.org/10.3390/a16040205
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  • [486]
    2023 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2968921
    Schilling, M., Hammer, B., Ohl, F. W., Ritter, H., & Wiskott, L. (2023). Modularity in Nervous Systems-a Key to Efficient Adaptivity for Deep Reinforcement Learning. Cognitive Computation. https://doi.org/10.1007/s12559-022-10080-w
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    2022 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2962746 OA
    Artelt, A., Hinder, F., Vaquet, V., Feldhans, R., & Hammer, B. (2022). Contrasting Explanations for Understanding and Regularizing Model Adaptations. Neural Processing Letters, 55, 5273–5297. https://doi.org/10.1007/s11063-022-10826-5
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  • [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, 8423–8436. https://doi.org/10.1007/s00521-022-08115-2
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  • [483]
    2022 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982135
    Jakob, J., Hasenjäger, M., & Hammer, B. (2022). Reject Options for Incremental Regression Scenarios. In E. Pimenidis, P. Angelov, C. Jayne, A. Papaleonidas, & M. Aydin (Eds.), Lecture Notes in Computer Science. Artificial Neural Networks and Machine Learning – ICANN 2022. 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings; Part IV (pp. 248-259). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-15937-4_21
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    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. 2022 ACM Conference on Fairness, Accountability, and Transparency, 2125-2137. New York, NY, USA: ACM. https://doi.org/10.1145/3531146.3534630
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  • [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, P. Angelov, C. Jayne, A. Papaleonidas, & M. Aydin (Eds.), Lecture Notes in Computer Science: Vol. 13532. Artificial Neural Networks and Machine Learning – ICANN 2022. 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings; Part IV (pp. 260-272). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-15937-4_22
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  • [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) (pp. 854-859). Piscataway, NJ: IEEE. https://doi.org/10.1109/SSCI51031.2022.10022139
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    2022 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2967296
    Velioglu, R., Göpfert, J. P., Artelt, A., & Hammer, B. (2022). Explainable Artificial Intelligence for Improved Modeling of Processes. In H. Yin, D. Camacho, & P. Tino (Eds.), Lecture Notes in Computer Science: Vol. 13756. Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings (pp. 313-325). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-21753-1_31
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    2022 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2969459
    Jakob, J., Artelt, A., Hasenjäger, M., & Hammer, B. (2022). SAM-kNN Regressor for Online Learning in Water Distribution Networks. In E. Pimenidis, P. Angelov, C. Jayne, A. Papaleonidas, & M. Aydin (Eds.), Lecture Notes in Computer Science: Vol. 13531. Artificial Neural Networks and Machine Learning – ICANN 2022. 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings, Part III (pp. 752-762). Cham: Springer Nature . https://doi.org/10.1007/978-3-031-15934-3_62
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    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969460
    Artelt, A., Brinkrolf, J., Visser, R., & Hammer, B. (2022). Explaining Reject Options of Learning Vector Quantization Classifiers. Proceedings of the 14th International Joint Conference on Computational Intelligence, 249-261. SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0011389600003332
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    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2966088
    Hinder, F., Vaquet, V., Brinkrolf, J., Artelt, A., & Hammer, B. (2022). Localization of Concept Drift: Identifying the Drifting Datapoints. 2022 International Joint Conference on Neural Networks (IJCNN), 1-9. IEEE. https://doi.org/10.1109/IJCNN55064.2022.9892374
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    2022 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2967410
    Vieth, M., Grimmelsmann, N., Schneider, A., & Hammer, B. (2022). Efficient Sensor Selection for Individualized Prediction Based on Biosignals. In H. Yin, D. Camacho, & P. Tino (Eds.), Lecture Notes in Computer Science: Vol. 13756. Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings (pp. 326-337). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-21753-1_32
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  • [474]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2966600
    Kenneweg, P., Schroeder, S., & Hammer, B. (2022). Neural Architecture Search for Sentence Classification with BERT. ESANN 2022 proceedings, 417-422. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com. https://doi.org/10.14428/esann/2022.ES2022-45
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    2022 | Konferenzbeitrag | Angenommen | PUB-ID: 2964534
    Vaquet, V., Hinder, F., Brinkrolf, J., Menz, P., Seiffert, U., & Hammer, B. (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.
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    Muschalik, M., Fumagalli, F., Hammer, B., & Hüllermeier, E. (2022). Agnostic Explanation of Model Change based on Feature Importance. KI - Künstliche Intelligenz. https://doi.org/10.1007/s13218-022-00766-6
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  • [471]
    2022 | Report | Veröffentlicht | PUB-ID: 2965286
    Artelt, A., Geminn, C., Hammer, B., Manzeschke, A., Mavrina, L., & Weber, C. (2022). Faire Algorithmen und die Fairness von Erklärungen: Informatische, rechtliche und ethische Perspektiven. DuEPublico: Duisburg-Essen Publications online, University of Duisburg-Essen, Germany. https://doi.org/10.17185/DUEPUBLICO/76311
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  • [470]
    2022 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2962928
    Vaquet, V., Menz, P., Seiffert, U., & Hammer, B. (2022). Investigating Intensity and Transversal Drift in Hyperspectral Imaging Data. Neurocomputing. https://doi.org/10.1016/j.neucom.2022.07.011
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    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2962650 OA
    Vaquet, V., Artelt, A., Brinkrolf, J., & Hammer, B. (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.
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    2022 | Kurzbeitrag Konferenz / Poster | PUB-ID: 2962861
    Hinder, F., Vaquet, V., Brinkrolf, J., Artelt, A., & Hammer, B. (2022). Localization of Concept Drift: Identifying the Drifting Datapoints. Presented at the
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    2022 | Preprint | PUB-ID: 2962919 OA
    Artelt, A., Vrachimis, S., Eliades, D., Polycarpou, M., & Hammer, B. (2022). One Explanation to Rule them All — Ensemble Consistent Explanations. ArXiv:2205.08974
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    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2967096
    Kenneweg, P., Schulz, A., Schroeder, S., & Hammer, B. (2022). Intelligent Learning Rate Distribution to Reduce Catastrophic Forgetting in Transformers. In H. Yin, D. Camacho, & P. Tino (Eds.), Lecture Notes in Computer Science: Vol. 13756. Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings (pp. 252-261). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-21753-1_25
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    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2987492
    Savic, D., Hammer, B., Koundouri, P., & Polycarpou, M. (2022). Long-Term Transitioning of Water Distribution Systems: ERC Water-Futures Project. Proceedings - 2nd International Join Conference on Water Distribution System Analysis (WDSA)& Computing and Control in the Water Industry (CCWI) València: Editorial Universitat Politècnica de València. https://doi.org/10.4995/WDSA-CCWI2022.2022.14441
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    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.), Lecture Notes in Computer Science. Advances in Intelligent Data Analysis XX. 20th International Symposium on Intelligent Data Analysis, IDA 2022, Rennes, France, April 20–22, 2022, Proceedings (pp. 157-170). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-01333-1_13
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    2022 | Zeitschriftenaufsatz | PUB-ID: 2978998
    Paaßen, B., Schulz, A., C. Stewart, T., & Hammer, B. (2022). Reservoir Memory Machines as Neural Computers. IEEE Transactions on Neural Networks and Learning Systems, 33(6), 2575–2585. https://doi.org/10.1109/TNNLS.2021.3094139
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    2022 | Report | Veröffentlicht | PUB-ID: 2965622 OA
    Hammer, B., Hüllermeier, E., Lohweg, V., Schneider, A., Schenck, W., Kuhl, U., Braun, M., 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. https://doi.org/10.4119/unibi/2965622
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  • [461]
    2022 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2964829
    Langnickel, L., Schulz, A., Hammer, B., & Fluck, J. (2022). BERT WEAVER: Using WEight AVERaging to Enable Lifelong Learning for Transformer-based Models. arXiv. https://doi.org/10.48550/ARXIV.2202.10101
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    2022 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2961873
    Göpfert, J. P., Wersing, H., & Hammer, B. (2022). Interpretable locally adaptive nearest neighbors. Neurocomputing, 470, 344-351. https://doi.org/10.1016/j.neucom.2021.05.105
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    2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2958662
    Schilling, M., Melnik, A., Ohl, F. W., Ritter, H., & Hammer, B. (2021). Decentralized control and local information for robust and adaptive decentralized Deep Reinforcement Learning. Neural Networks, 144, 699-725. https://doi.org/10.1016/j.neunet.2021.09.017
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    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982134
    Castellani, A., Schmitt, S., & Hammer, B. (2021). Task-Sensitive Concept Drift Detector with Constraint Embedding. 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 01-08. IEEE. https://doi.org/10.1109/SSCI50451.2021.9659969
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    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. 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 1-8. IEEE. https://doi.org/10.1109/SSCI50451.2021.9660138
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    2021 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982165
    Liuliakov, A., & Hammer, B. (2021). AutoML Technologies for the Identification of Sparse Models. In H. Yin, D. Camacho, P. Tino, R. Allmendinger, A. J. Tallón-Ballesteros, K. Tang, S. - B. Cho, et al. (Eds.), Lecture Notes in Computer Science: Vol. 13113. Intelligent Data Engineering and Automated Learning – IDEAL 2021. 22nd International Conference, IDEAL 2021, Manchester, UK, November 25–27, 2021, Proceedings (pp. 65-75). Cham: Springer . https://doi.org/10.1007/978-3-030-91608-4_7
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    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, F. Pérez-Cruz, S. Kramer, J. Read, & J. A. Lozano (Eds.), Lecture Notes in Computer Science: Vol. 12975. Machine Learning and Knowledge Discovery in Databases. Research Track. European Conference, ECML PKDD 2021, Bilbao, Spain, September 13–17, 2021, Proceedings, Part I (pp. 469-484). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-86486-6_29
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    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), 304-317. https://doi.org/10.1016/j.neucom.2021.04.129
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    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2957373
    Artelt, A., Hinder, F., Vaquet, V., Feldhans, R., & Hammer, B. (2021). Contrastive Explanations for Explaining Model Adaptations. In I. Rojas, G. Joya, & A. Catala (Eds.), Lecture Notes in Computer Science. Advances in Computational Intelligence. 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Virtual Event, June 16–18, 2021, Proceedings, Part I (pp. 101-112). Cham: Springer . https://doi.org/10.1007/978-3-030-85030-2_9
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    2021 | Preprint | PUB-ID: 2959899
    Artelt, A., & Hammer, B. (2021). Convex optimization for actionable & plausible counterfactual explanations. arXiv: 2105.07630v1
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    2021 | Konferenzbeitrag | PUB-ID: 2959428
    Hinder, F., Vaquet, V., Brinkrolf, J., & Hammer, B. (2021). Fast Non-Parametric Conditional Density Estimation using Moment Trees. IEEE Computational Intelligence Magazine
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    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2960754
    Hinder, F., Brinkrolf, J., Vaquet, V., & Hammer, B. (2021). A Shape-Based Method for Concept Drift Detection and Signal Denoising. 2021 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings, 01-08. Piscataway, NJ: IEEE. https://doi.org/10.1109/SSCI50451.2021.9660111
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    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2960755
    Hinder, F., Vaquet, V., Brinkrolf, J., & Hammer, B. (2021). Fast Non-Parametric Conditional Density Estimation using Moment Trees. 2021 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings, 1-7. Piscataway, NJ: IEEE. https://doi.org/10.1109/SSCI50451.2021.9660031
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    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2960685
    Vaquet, V., Menz, P., Seiffert, U., & Hammer, B. (2021). Investigating Intensity and Transversal Drift in Hyperspectral Imaging Data. In M. Verleysen (Ed.), ESANN 2021 proceedings (pp. 47-52). Louvain-la-Neuve (Belgium): Ciaco - i6doc.com. https://doi.org/10.14428/esann/2021.ES2021-64
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    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2960687
    Vaquet, V., Hinder, F., Vaquet, J., Brinkrolf, J., & Hammer, B. (2021). Online Learning on Non-Stationary Data Streams for Image Recognition using Deep Embeddings. IEEE Symposium Series on Computational Intelligence, 1-7. https://doi.org/10.1109/SSCI50451.2021.9659903
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    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2962747
    Artelt, A., Vaquet, V., Velioglu, R., Hinder, F., Brinkrolf, J., Schilling, M., & Hammer, B. (2021). Evaluating Robustness of Counterfactual Explanations. 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 01-09. Piscataway, NJ: IEEE. https://doi.org/10.1109/SSCI50451.2021.9660058
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    2021 | Report | Veröffentlicht | PUB-ID: 2954239
    Szczuka, J., Artelt, A., Geminn, C., Hammer, B., Kopp, S., Manzeschke, A., Rossnagel, A., et al. (2021). Können Kinder aufgeklärte Nutzer* innen von Sprachassistenten sein? Rechtliche, psychologische, ethische und informatische Perspektiven. Essen: Universität Duisburg-Essen, Universitätsbibliothek. https://doi.org/10.17185/duepublico/74238
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    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2957588
    Artelt, A., & Hammer, B. (2021). Efficient computation of contrastive explanations. 2021 International Joint Conference on Neural Networks (IJCNN), 1-9. New York: Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/IJCNN52387.2021.9534454
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  • [443]
    2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2949334 OA
    Rohlfing, K., Cimiano, P., Scharlau, I., Matzner, T., Buhl, H. M., Buschmeier, H., Esposito, E., 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), 717--728. https://doi.org/10.1109/TCDS.2020.3044366
    PUB | PDF | DOI | WoS
     
  • [442]
    2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2954542
    Paaßen, B., Schulz, A., & Hammer, B. (2021). Reservoir Stack Machines. Neurocomputing, 470, 352-364. https://doi.org/10.1016/j.neucom.2021.05.106
    PUB | DOI | Download (ext.) | WoS | arXiv
     
  • [441]
    2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2959418
    Göpfert, J. P., Kuhl, U., Hindemith, L., Wersing, H., & Hammer, B. (2021). Intuitiveness in Active Teaching. IEEE Transactions on Human-Machine Systems, 1-10. https://doi.org/10.1109/THMS.2021.3121666
    PUB | DOI | WoS
     
  • [440]
    2021 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2956229
    Paassen, B., Schulz, A., Stewart, T. C., & Hammer, B. (2021). Reservoir Memory Machines as Neural Computers. IEEE Transactions on Neural Networks and Learning Systems, 1-11. https://doi.org/10.1109/TNNLS.2021.3094139
    PUB | DOI | Download (ext.) | WoS | PubMed | Europe PMC | arXiv
     
  • [439]
    2021 | Zeitschriftenaufsatz | Angenommen | PUB-ID: 2955245
    Stallmann, D., Göpfert, J. P., Schmitz, J., Grünberger, A., & Hammer, B. (Accepted). Towards an automatic analysis of CHO-K1 suspension growth in microfluidic single-cell cultivation. Bioinformatics . https://doi.org/10.1093/bioinformatics/btab386
    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. ESANN 2021 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. , 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
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  • [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., Schulz, A., Redick, T., Ayoub, N., Modabber, A., Abel, D., & Hammer, B. (2021). Efficient Reject Options for Particle Filter Object Tracking in Medical Applications. Sensors, 21(6), 2114. https://doi.org/10.3390/s21062114
    PUB | PDF | DOI | WoS | PubMed | Europe PMC
     
  • [434]
    2021 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2955115
    Straat, M., Abadi, F., Kan, Z., Göpfert, C., Hammer, B., & Biehl, M. (2021). Supervised learning in the presence of concept drift: a modelling framework. Neural Computing and Applications. https://doi.org/10.1007/s00521-021-06035-1
    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.), Lecture Notes in Computer Science: Vol. 12397. Artificial Neural Networks and Machine Learning – ICANN 2020. 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part II (pp. 850-862). Cham: Springer. https://doi.org/10.1007/978-3-030-61616-8_68
    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). 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 (pp. 19-24). Louvain-la-Neuve: Ciaco .
    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.), Lecture Notes in Computer Science: Vol. 12396. Artificial Neural Networks and Machine Learning - ICANN 2020 - 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15-18, 2020, Proceedings, Part {I} (pp. 353-365). Cham: Springer. https://doi.org/10.1007/978-3-030-61609-0_28
    PUB | DOI | Download (ext.)
     
  • [429]
    2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2957814
    Krämer, N., Szczuka, J., Rossnagel, A., Geminn, C., Kopp, S., Hammer, B., Mavrina, L., et al. (2020). Improving and Evaluating Conversational User Interfaces for Children. IUI '20: Proceedings of the 25th International Conference on Intelligent User Interfaces New York: Association for Computing Machinery.
    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. Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, {IJCAI-20}. https://doi.org/10.24963/ijcai.2020/319
    PUB | DOI | Download (ext.) | arXiv
     
  • [427]
    2020 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982081
    Biehl, M., Abadi, F., Göpfert, C., & Hammer, B. (2020). Prototype-Based Classifiers in the Presence of Concept Drift: A Modelling Framework. In A. Vellido, K. Gibert, C. Angulo, & J. D. Martín Guerrero (Eds.), Advances in Intelligent Systems and Computing. 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 (pp. 210-221). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-19642-4_21
    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., Jakob, J., Hinder, F., Biehl, M., Tino, P., & Hammer, B. (2020). Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information. Neurocomputing. doi:10.1016/j.neucom.2019.12.133
    PUB | DOI | Download (ext.) | WoS | arXiv
     
  • [424]
    2020 | Report | Veröffentlicht | PUB-ID: 2946614 OA
    Hammer, B., van der Aalst, W., Bauckhage, C., Behnke, S., Holz, T., Krämer, N., Morik, K., et al. (2020). Sustainability and Trust for Artificial Intelligence Technologies. doi:10.4119/unibi/2946614
    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. doi:10.1007/s00521-020-04888-6
    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, V. Kůrková, P. Karpov, & F. Theis (Eds.), Lecture Notes in Computer Science. 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 (pp. 302-311). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-30487-4_24
    PUB | DOI
     
  • [419]
    2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982084
    Losing, V., Yoshikawa, T., Hasenjaeger, M., Hammer, B., & Wersing, H. (2019). Personalized Online Learning of Whole-Body Motion Classes using Multiple Inertial Measurement Units. 2019 International Conference on Robotics and Automation (ICRA), 9530-9536. IEEE. https://doi.org/10.1109/ICRA.2019.8794251
    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. 2019 International Joint Conference on Neural Networks (IJCNN), 1-8. IEEE. https://doi.org/10.1109/IJCNN.2019.8851982
    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. 2019 IEEE International Conference on Industrial Technology (ICIT), 1311-1316. IEEE. https://doi.org/10.1109/ICIT.2019.8754997
    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., Schulz, A., Paaßen, B., Schoisswohl, J., Kaniusas, E., Dorffner, G., Hammer, B., et al. (2019). Counteracting Electrode Shifts in Upper-Limb Prosthesis Control via Transfer Learning. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(5), 956-962. doi:10.1109/TNSRE.2019.2907200
    PUB | PDF | DOI | WoS | PubMed | Europe PMC
     
  • [414]
    2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2933893
    Pfannschmidt, L., Jakob, J., Biehl, M., Tino, P., & Hammer, B. (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., Artelt, A., Geminn, C., Hammer, B., Kopp, S., Manzeschke, A., Rossnagel, A., et al. (2019). KI-basierte Sprachassistenten im Alltag: Forschungsbedarf aus informatischer, psychologischer, ethischer und rechtlicher Sicht. Universität Duisburg-Essen, Universitätsbibliothek. doi:10.17185/duepublico/70571
    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., Göpfert, C., Neumann, U., Heider, D., & Hammer, B. (2019). FRI - Feature Relevance Intervals for Interpretable and Interactive Data Exploration. Presented at the 16th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, Certosa di Pontignano, Siena - Tuscany, Italy. doi:10.1109/CIBCB.2019.8791489
    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, 125-136. doi:10.1016/j.neucom.2018.11.095
    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. doi:10.4119/unibi/2934181
    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. doi:10.1007/s00521-018-03966-0
    PUB | DOI | WoS
     
  • [403]
    2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2931283 OA
    Queißer, J., Ishihara, H., Hammer, B., Steil, J. J., & Asada, M. (2018). Skill Memories for Parameterized Dynamic Action Primitives on the Pneumatically Driven Humanoid Robot Child Affetto. Presented at the International Conference on Development and Learning and on Epigenetic Robotics 2018 (ICDL-EPIROB2018), Tokyo .
    PUB | PDF
     
  • [402]
    2018 | Datenpublikation | PUB-ID: 2930611 OA
    Hülsmann, F., Göpfert, J. P., Hammer, B., Kopp, S., & Botsch, M. (2018). Classification of motor errors to provide real-time feedback for sports coaching in virtual reality - A case study in squats and Tai Chi pushes (Data). Bielefeld University. https://doi.org/10.4119/unibi/2930611
    PUB | Dateien verfügbar | DOI
     
  • [401]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2930862
    Hülsmann, F., Göpfert, J. P., Hammer, B., Kopp, S., & Botsch, M. (2018). Classification of motor errors to provide real-time feedback for sports coaching in virtual reality — A case study in squats and Tai Chi pushes. Computers & Graphics, 76, 47-59. https://doi.org/10.1016/j.cag.2018.08.003
    PUB | DOI | Download (ext.) | WoS
     
  • [400]
    2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982092
    Queisser, J. F., Hammer, B., Ishihara, H., Asada, M., & Steil, J. J. (2018). Skill Memories for Parameterized Dynamic Action Primitives on the Pneumatically Driven Humanoid Robot Child Affetto. 2018 Joint IEEE 8th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), 39-45. IEEE. https://doi.org/10.1109/DEVLRN.2018.8761040
    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.), Lecture Notes in Computer Science. Advances in Intelligent Data Analysis XVII. 17th International Symposium, IDA 2018, ’s-Hertogenbosch, The Netherlands, October 24–26, 2018, Proceedings (pp. 137-150). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-01768-2_12
    PUB | DOI
     
  • [398]
    2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982089
    Specht, F., Otto, J., Niggemann, O., & Hammer, B. (2018). Generation of Adversarial Examples to Prevent Misclassification of Deep Neural Network based Condition Monitoring Systems for Cyber-Physical Production Systems. 2018 IEEE 16th International Conference on Industrial Informatics (INDIN), 760-765. IEEE. https://doi.org/10.1109/INDIN.2018.8472060
    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. 2018 IEEE International Conference on Data Mining (ICDM), 287-296. IEEE. https://doi.org/10.1109/ICDM.2018.00044
    PUB | DOI
     
  • [396]
    2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982087
    Hosseini, B., & Hammer, B. (2018). Confident Kernel Sparse Coding and Dictionary Learning. 2018 IEEE International Conference on Data Mining (ICDM), 1031-1036. IEEE. https://doi.org/10.1109/ICDM.2018.00130
    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. IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, 5345-5352. IEEE. https://doi.org/10.1109/IECON.2018.8592906
    PUB | DOI
     
  • [394]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2932412
    Straat, M., Abadi, F., Göpfert, C., Hammer, B., & Biehl, M. (2018). Statistical Mechanics of On-Line Learning Under Concept Drift. ENTROPY, 20(10), 775. doi:10.3390/e20100775
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [393]
    2018 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2917896
    Lux, M., Brinkman, R. R., Chauve, C., Laing, A., Lorenc, A., Abeler-Dörner, L., & Hammer, B. (2018). flowLearn: Fast and precise identification and quality checking of cell populations in flow cytometry. Bioinformatics, 34(13), 2245-2253. doi:10.1093/bioinformatics/bty082
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [392]
    2018 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2933557
    Meyer, S., Bertrand, O., Egelhaaf, M., & Hammer, B. (2018). Inferring Temporal Structure from Predictability in Bumblebee Learning Flight. In H. Yin, D. Camacho, P. Novais, & A. J. Tallón-Ballesteros (Eds.), Lecture Notes in Computer Science: Vol. 11314. Intelligent Data Engineering and Automated Learning – IDEAL 2018 (pp. 508-519). Cham: Springer International Publishing. doi:10.1007/978-3-030-03493-1_53
    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), 669-689. doi:10.1007/s11063-017-9684-5
    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. 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. ESANN 2018. Proceedings of 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 219-224.
    PUB | arXiv
     
  • [386]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2914505
    Paaßen, B., Schulz, A., Hahne, J., & Hammer, B. (2018). Expectation maximization transfer learning and its application for bionic hand prostheses. Neurocomputing, 298, 122-133. doi:10.1016/j.neucom.2017.11.072
    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, Y. Manolopoulos, B. Hammer, L. Iliadis, & I. Maglogiannis (Eds.), Lecture Notes in Computer Science: Vol. 11139. Artificial Neural Networks and Machine Learning – ICANN 2018. Proceedings, Part I Cham: Springer. doi:10.1007/978-3-030-01418-6_45
    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), 171-201. doi:10.1007/s10115-017-1137-y
    PUB | DOI | WoS
     
  • [383]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2915273 OA
    Göpfert, C., Pfannschmidt, L., Göpfert, J. P., & Hammer, B. (2018). Interpretation of Linear Classifiers by Means of Feature Relevance Bounds. Neurocomputing, 298, 69-79. doi:10.1016/j.neucom.2017.11.074
    PUB | PDF | DOI | WoS
     
  • [382]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2913389
    Paaßen, B., Hammer, B., Price, T., Barnes, T., Gross, S., & Pinkwart, N. (2018). The Continuous Hint Factory - Providing Hints in Vast and Sparsely Populated Edit Distance Spaces. Journal of Educational Data Mining, 10(1), 1-35.
    PUB | Download (ext.) | arXiv
     
  • [381]
    2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2919844
    Paaßen, B., Gallicchio, C., Micheli, A., & Hammer, B. (2018). Tree Edit Distance Learning via Adaptive Symbol Embeddings. In J. Dy & A. Krause (Eds.), Proceedings of Machine Learning Research: Vol. 80. Proceedings of the 35th International Conference on Machine Learning (ICML 2018) (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, 1261-1274. doi:10.1016/j.neucom.2017.06.084
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    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2918244
    Brinkrolf, J., & Hammer, B. (2018). Interpretable Machine Learning with Reject Option. at - Automatisierungstechnik, 66(4), 283-290. doi:10.1515/auto-2017-0123
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    2018 | Konferenzbeitrag | PUB-ID: 2916318
    Berger, K., Schulz, A., Paaßen, B., & Hammer, B. (2018). Linear Supervised Transfer Learning for the Large Margin Nearest Neighbor Classifier. Presented at the SSCI CIDM 2017. doi:10.1109/SSCI.2017.8285359
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    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982095
    Frenay, B., & Hammer, B. (2017). Label-noise-tolerant classification for streaming data. 2017 International Joint Conference on Neural Networks (IJCNN), 1748-1755. IEEE. https://doi.org/10.1109/IJCNN.2017.7966062
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    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982091
    Losing, V., Hammer, B., & Wersing, H. (2017). Personalized maneuver prediction at intersections. 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 1-6. IEEE. https://doi.org/10.1109/ITSC.2017.8317760
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    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.
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    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909369 OA
    Paaßen, B., Schulz, A., Hahne, J., & Hammer, B. (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) (pp. 129-134). Bruges: i6doc.com.
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    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2914945
    Brinkrolf, J., & Hammer, B. (2017). Probabilistic extension and reject options for pairwise LVQ. 2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM) Piscataway, NJ: IEEE. doi:10.1109/WSOM.2017.8020028
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    2017 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2909372 OA
    Schulz, A., Brinkrolf, J., & Hammer, B. (2017). Efficient Kernelization of Discriminative Dimensionality Reduction. Neurocomputing, 268(SI), 34-41. doi:10.1016/j.neucom.2017.01.104
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    2017 | Konferenzbeitrag | PUB-ID: 2909371
    Biehl, M., Hammer, B., & Villmann, T. (2017). Prototype based models for the supervised learning of classificaton schemes. Proc. of the IAU Symposium 325 on Astroinformatics, Sorrento/Italy, October 2016, in press.
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    2017 | Konferenzbeitrag | PUB-ID: 2914950
    Brinkrolf, J., Berger, K., & Hammer, B. (2017). Differential Privacy for Learning Vector Quantization. New Challenges in Neural Computation
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    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. doi:10.24963/ijcai.2017/690
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    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 (pp. 187--192). Louvain-la-Neuve: Ciaco - i6doc.com.
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    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.
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    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2913752 OA
    Göpfert, J. P., Göpfert, C., Botsch, M., & Hammer, B. (2017). Effects of Variability in Synthetic Training Data on Convolutional Neural Networks for 3D Head Reconstruction. 2017 SSCI Proceedings. 2017 IEEE Symposium Series on Computational Intelligence (SSCI) Piscataway, NJ: IEEE. doi:10.1109/SSCI.2017.8285305
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    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.
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    2017 | Konferenzbeitrag | PUB-ID: 2909370
    Frenay, B., & Hammer, B. (2017). Label-Noise-Tolerant Classification for Streaming Data. IEEE International Joint Conference on Neural Neworks
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    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2914141 OA
    Aswolinskiy, W., & Hammer, B. (2017). Unsupervised Transfer Learning for Time Series via Self-Predictive Modelling - First Results. Proceedings of the Workshop on New Challenges in Neural Computation (NC2), Machine Learning Reports, 03/2017 Bielefeld: Universität Bielefeld, CITEC.
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    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909037 OA
    Prahm, C., Schulz, A., Paaßen, B., Aszmann, O., Hammer, B., & Dorffner, G. (2017). Echo State Networks as Novel Approach for Low-Cost Myoelectric Control. In A. ten Telje, C. Popow, J. H. Holmes, & L. Sacchi (Eds.), Lecture Notes in Computer Science: Vol. 10259. Proceedings of the 16th Conference on Artificial Intelligence in Medicine (AIME 2017) (pp. 338--342). Springer. doi:10.1007/978-3-319-59758-4_40
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    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. Proceedings of the 2017 NIPS workshop on Transparent and Interpretable Machine Learning in Safety Critical Environments
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    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2904469
    Hosseini, B., Hülsmann, F., Botsch, M., & Hammer, B. (2016). Non-Negative Kernel Sparse Coding for the Analysis of Motion Data. In A. E.P. Villa, P. Masulli, & A. Javier Pons Rivero (Eds.), Lecture Notes in Computer Science: Vol. 9887. Artificial Neural Networks and Machine Learning – ICANN 2016 (pp. 506-514). Cham: Springer. https://doi.org/10.1007/978-3-319-44781-0_60
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    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), 129-138. https://doi.org/10.1017/S1743921316012928
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    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982096
    Fischer, L., Hammer, B., & Wersing, H. (2016). Online metric learning for an adaptation to confidence drift. 2016 International Joint Conference on Neural Networks (IJCNN), 748-755. IEEE. https://doi.org/10.1109/IJCNN.2016.7727275
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    2016 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2907633 OA
    Lux, M., Krüger, J., Rinke, C., Maus, I., Schlüter, A., Woyke, T., Sczyrba, A., et al. (2016). acdc – Automated Contamination Detection and Confidence estimation for single-cell genome data. BMC Bioinformatics, 17(1), 543. doi:10.1186/s12859-016-1397-7
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    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909367
    Kummert, J., Paaßen, B., Jensen, J., Göpfert, C., & Hammer, B. (2016). Local Reject Option for Deterministic Multi-class SVM. In A. E.P. Villa, P. Masulli, & A. J. Pons Rivero (Eds.), Lecture Notes in Computer Science: Vol. 9887. Artificial Neural Networks and Machine Learning - ICANN 2016 - 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II (pp. 251--258). Cham: Springer Nature. doi:10.1007/978-3-319-44781-0_30
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    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), 3-13. doi:10.1016/j.neucom.2015.12.108
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    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 (pp. 183-190). Raleigh, North Carolina, USA: International Educational Datamining Society.
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    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 (pp. 41--46). Louvain-la-Neuve: Ciaco - i6doc.com.
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    2016 | Konferenzbeitrag | E-Veröff. vor dem Druck | PUB-ID: 2904909 OA
    Schulz, A., & Hammer, B. (2016). Discriminative Dimensionality Reduction in Kernel Space. ESANN2016 Proceedings. 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges, Belgium,27-29 April 2016
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    2016 | Konferenzbeitrag | PUB-ID: 2909365
    Brinkrolf, J., Mittag, T., Joppen, R., Dr\, A., Pietsch, K. - H., & Hammer, B. (2016). Virtual optimisation for improved production planning. New Challenges in Neural Computation
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    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.
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    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.), Lecture Notes in Computer Science: Vol. 9887. Artificial Neural Networks and Machine Learning – ICANN 2016: 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II (pp. 510-517). Cham: Springer Nature. doi:10.1007/978-3-319-44778-0_60
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    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.
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    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.), Machine Learning Reports. Proceedings of the Workshop New Challenges in Neural Computation 2016 (pp. 11-18).
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    2016 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2903457
    Schleif, F. - M., Hammer, B., Gonzalez Monroy, J., Gonzalez Jimenez, J., Blanco-Claraco, J. - L., Biehl, M., & Petkov, N. (2016). Odor recognition in robotics applications by discriminative time-series modeling. PATTERN ANALYSIS AND APPLICATIONS, 19(1), 207-220. doi:10.1007/s10044-014-0442-2
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    2016 | Konferenzbeitrag | PUB-ID: 2909368
    Geppert, er, & Hammer, B. (2016). Incremental learning algorithms and applications. ESANN
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    2016 | Konferenzbeitrag | PUB-ID: 2905195
    Fischer, L., Hammer, B., & Wersing, H. (2016). Online Metric Learning for an Adaptation to Confidence Drift. Proceedings of International Joint Conference on Neural Networks (IJCNN), 748-755
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    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2904178 OA
    Prahm, C., Paaßen, B., Schulz, A., Hammer, B., & Aszmann, O. (2016). Transfer Learning for Rapid Re-calibration of a Myoelectric Prosthesis after Electrode Shift. In J. Ibáñez, J. Gonzáles-Vargas, J. M. Azorín, M. Akay, & J. L. Pons (Eds.), Converging Clinical and Engineering Research on Neurorehabilitation II: Proceedings of the 3rd International Conference on NeuroRehabilitation (ICNR2016) (pp. 153--157). Springer. doi:10.1007/978-3-319-46669-9_28
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    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2907622 OA
    Losing, V., Hammer, B., & Wersing, H. (2016). KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift. 2016 IEEE 16th International Conference on Data Mining (ICDM), 291-300. Piscataway, NJ: IEEE. doi:10.1109/ICDM.2016.0040
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    2016 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2910957
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    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909366
    Villmann, T., Kaden, M., Bohnsack, A., Villmann, J. M., Drogies, T., Saralajew, S., & Hammer, B. (2016). Self-Adjusting Reject Options in Prototype Based Classification. In E. Merényi, M. J. Mendenhall, & P. O'Driscoll (Eds.), Advances in Intelligent Systems and Computing: Vol. 428. Advances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 11th International Workshop WSOM 2016, Houston, Texas, USA, January 6-8, 2016 (pp. 269-279). Cham: Springer International Publishing. doi:10.1007/978-3-319-28518-4_24
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    2016 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2905193
    Fischer, L., Hammer, B., & Wersing, H. (2016). Optimal local rejection for classifiers. Neurocomputing, 214, 445-457. doi:10.1016/j.neucom.2016.06.038
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    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982098
    Fischer, L., Hammer, B., & Wersing, H. (2015). Combining offline and online classifiers for life-long learning. 2015 International Joint Conference on Neural Networks (IJCNN), 1-8. IEEE. https://doi.org/10.1109/IJCNN.2015.7280678
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    2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2752948 OA
    Gross, S., Mokbel, B., Hammer, B., & Pinkwart, N. (2015). Learning Feedback in Intelligent Tutoring Systems. Report of the FIT Project, Conducted from December 2011 to March 2015. KI - Künstliche Intelligenz, 29(4), 413-418. https://doi.org/10.1007/s13218-015-0367-y
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    2015 | Preprint | Veröffentlicht | PUB-ID: 2901613
    Lux, M., Hammer, B., & Sczyrba, A. (2015). Automated Contamination Detection in Single-Cell Sequencing. bioRxiv
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    2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2671047 OA
    Gisbrecht, A., Schulz, A., & Hammer, B. (2015). Parametric nonlinear dimensionality reduction using kernel t-SNE. Neurocomputing, 147, 71-82. doi:10.1016/j.neucom.2013.11.045
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    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), 51-73. doi:10.1002/widm.1147
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    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), 95-116. doi:10.1007/s10472-014-9402-1
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    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2783165
    Hosseini, B., & Hammer, B. (2015). Efficient Metric Learning for the Analysis of Motion Data. 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA) Piscataway, NJ: IEEE. doi:10.1109/DSAA.2015.7344819
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    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2903777 OA
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    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).
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    2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2710031 OA
    Mokbel, B., Paaßen, B., Schleif, F. - M., & Hammer, B. (2015). Metric learning for sequences in relational LVQ. Neurocomputing, 169(SI), 306-322. doi:10.1016/j.neucom.2014.11.082
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    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), 27-54. doi:10.1007/s11063-014-9394-1
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    2015 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2900303 OA
    Schulz, A., & Hammer, B. (2015). Visualization of Regression Models Using Discriminative Dimensionality Reduction. Computer Analysis of Images and Patterns, Lecture Notes in Computer Science, 9257, 437-449. Cham: Springer Science + Business Media. doi:10.1007/978-3-319-23117-4_38
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    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 (pp. 507-512). Louvain-la-Neuve: Ciaco.
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    2015 | Zeitschriftenaufsatz | PUB-ID: 2909364
    Hammer, B., & Toussaint, M. (2015). Special Issue on Autonomous Learning. {KI}, 29(4), 323--327. doi:10.1007/s13218-015-0392-x
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    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900319
    Schulz, A., & Hammer, B. (2015). Discriminative dimensionality reduction for regression problems using the Fisher metric. 2015 International Joint Conference on Neural Networks (IJCNN), 1-8. doi:10.1109/ijcnn.2015.7280736
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    Fischer, L., Hammer, B., & Wersing, H. (2015). Optimum Reject Options for Prototype-based Classification
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    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2774707
    Fischer, L., Hammer, B., & Wersing, H. (2015). Certainty-based Prototype Insertion/Deletion for Classification with Metric Adaptation. ESANN, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 7-12
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    2015 | Konferenzbeitrag | PUB-ID: 2774721
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    2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2772407
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    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, J. G. Boticario, C. Romero, M. Pechenizkiy, A. Merceron, P. Mitros, J. M. Luna, et al. (Eds.), Proceedings of the 8th International Conference on Educational Data Mining (pp. 632-632). International Educational Datamining Society.
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    2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2752955 OA
    Walter, O., Häb-Umbach, R., Mokbel, B., Paaßen, B., & Hammer, B. (2015). Autonomous Learning of Representations. KI - Künstliche Intelligenz, 29(4), 339–351. doi:10.1007/s13218-015-0372-1
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    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, 96-106. doi:10.1016/j.neucom.2013.11.044
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    2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2772413
    Fischer, L., Hammer, B., & Wersing, H. (2015). Efficient rejection strategies for prototype-based classification. Neurocomputing, 169(SI), 334-342. doi:10.1016/j.neucom.2014.10.092
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    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2901612
    Lux, M., Sczyrba, A., & Hammer, B. (2015). Automatic discovery of metagenomic structure. 2015 International Joint Conference on Neural Networks (IJCNN) Institute of Electrical & Electronics Engineers (IEEE). doi:10.1109/ijcnn.2015.7280500
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    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900318
    Schulz, A., & Hammer, B. (2015). Metric Learning in Dimensionality Reduction. Proceedings of the International Conference on Pattern Recognition Applications and Methods, 232-239. doi:10.5220/0005200802320239
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    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. doi:10.1109/IJCNN.2015.7280610
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    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2910954
    Biehl, M., Hammer, B., Schleif, F. - M., Schneider, P., & Villmann, T. (2015). Stationarity of Matrix Relevance LVQ. 2015 International Joint Conference on Neural Networks (IJCNN) IEEE. doi:10.1109/ijcnn.2015.7280441
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    Bunte, K., Hammer, B., & Biehl, M. (2009). Nonlinear dimension reduction and visualization of labeled data. In X. Jiang & N. Petkov (Eds.), Lecture Notes in Computer Science, 5702: Vol. 5702. International Conference on Computer Analysis of Images and Patterns (pp. 1162-1170). Berlin: Springer. https://doi.org/10.1007/978-3-642-03767-2_141
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    2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993429
    Geweniger, T., Zühlke, D., Hammer, B., & Villmann, T. (2009). Median variant of fuzzy-c-means. In M. Verleysen (Ed.), European Symposium on Artificial Neural Networks (pp. 523-528). Evere: d-side publications.
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    Mokbel, B., Hasenfuss, A., & Hammer, B. (2009). Graph-based Representation of Symbolic Musical Data. In A. Torsello, F. Escolano, L. Brun, & International Association for Pattern Recognition. Technical Committee on Graph Based Representations (Eds.), Lecture notes in computer science: Vol. 5534. Graph-Based Representation in Pattern Recognition (GbRPR 2009). Lecture Notes in Computer Science, 5534 (pp. 42-51). Berlin: Springer. https://doi.org/10.1007/978-3-642-02124-4_5
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    2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994004
    Schneider, P., Biehl, M., & Hammer, B. (2009). Adaptive relevance matrices in learning vector quantization. Neural Computation, 21(12), 3532-3561. https://doi.org/10.1162/neco.2009.11-08-908
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    Bunte, K., Biehl, M., & Hammer, B. (2009). Nonlinear discriminative data visualization. In M. Verleysen (Ed.), European Symposium on Artificial Neural Networks (pp. 65-70). Evere: d-side publications.
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    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).
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    2009 | Herausgeber*in Sammelwerk | Veröffentlicht | PUB-ID: 1994316
    Biehl M., Hammer B., Verleysen M., & Villmann T. (Eds.) (2009). Similarity Based Clustering (Springer Lecture Notes Artificial Intelligence, 5400). Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-642-01805-3
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    2008 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982119
    Arnonkijpanich, B., Hammer, B., Hasenfuss, A., & Lursinsap, C. (2008). Matrix Learning for Topographic Neural Maps. In V. Kůrková, R. Neruda, & J. Koutník (Eds.), Lecture Notes in Computer Science. Artificial Neural Networks - ICANN 2008 (pp. 572-582). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-87536-9_59
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    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 (pp. 1337-1342). IGI Global.
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    2008 | Konferenzband | Veröffentlicht | PUB-ID: 1994329
    de Raedt L., Hammer B., Hitzler P., & Maass W. (Eds.) (2008). Recurrent Neural Networks - Models, Capacities, and Applications (8041). Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI).
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    2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993282
    Arnonkijpanich, B., Hammer, B., Hasenfuss, A., & Lursinsap, C. (2008). Matrix Learning for Topographic Neural Maps. In V. Kurková, R. Neruda, & J. Koutn'ık (Eds.), ICANN (1). Lecture Notes in Computer Science, 5163 (pp. 572-582). Berlin: Springer.
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    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 (pp. 227-232). Evere, Belgium: d-side publications.
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    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.
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    2008 | Report | Veröffentlicht | PUB-ID: 1993379
    Bunte, K., Schneider, P., Hammer, B., Schleif, F. - M., Villmann, T., & Biehl, M. (2008). Discriminative Visualization by Limited Rank Matrix Learning (Machine Learning Reports). Leipzig: Universität Leipzig.
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    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.
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    Witoelar, A., Biehl, M., Ghosh, A., & Hammer, B. (2008). Learning dynamics and robustness of vector quantization and neural gas. Neurocomputing, 71(7-9), 1210-1219. https://doi.org/10.1016/j.neucom.2007.11.022
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    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) (pp. 525-532). ASME Press. https://doi.org/10.1115/1.802823.paper66
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    2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993788
    Hasenfuss, A., & Hammer, B. (2008). Single Pass Clustering and Classification of Large Dissimilarity Datasets. In B. Prasad, P. Sinha, A. Ram, & E. E. Kerre (Eds.), Artificial Intelligence and Pattern Recognition (pp. 219-223). ISRST.
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    Schleif, F. - M., Villmann, T., & Hammer, B. (2008). Prototype based Fuzzy Classification in Clinical Proteomics. International Journal of Approximate Reasoning, 47(1), 4-16. https://doi.org/10.1016/j.ijar.2007.03.005
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    2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994072
    Strickert, M., Schneider, P., Keilwagen, J., Villmann, T., Biehl, M., & Hammer, B. (2008). Discriminatory Data Mapping by Matrix-Based Supervised Learning Metrics. In L. Prevost, S. Marinai, & F. Schwenker (Eds.), Lecture Notes in Computer Science, 5064. Artificial Neural Networks in Pattern Recognition. Third IAPR Workshop. Proceedings (pp. 78-89). Berlin: Springer. https://doi.org/10.1007/978-3-540-69939-2_8
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    2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994089
    Strickert, M., Sreenivasulu, N., Villmann, T., & Hammer, B. (2008). Robust Centroid-Based Clustering using Derivatives of Pearson Correlation. In P. Encarnação & A. Veloso (Eds.), BIOSIGNALS (2) (pp. 197-203). INSTICC - Institute for Systems and Technologies of Information, Control and Communication.
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    Winkler, T., Drieseberg, J., Hasenfuß, A., Hammer, B., & Hormann, K. (2008). Thinning Mesh Animations. In O. Deussen, D. Keim, & D. Saupe (Eds.), Proceedings of Vision, Modeling, and Visualization 2008 (pp. 149-158). Konstanz, Germany: Aka.
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    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 (pp. 1-12). Berlin: Springer. https://doi.org/10.1007/978-3-540-69939-2_1
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    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 (pp. 141-167). Berlin: Springer. https://doi.org/10.1007/978-3-540-70778-3_6
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    Hasenfuss, A., Hammer, B., Geweniger, T., & Villmann, T. (2008). Magnification Control in Relational Neural Gas. In M. Verleysen (Ed.), European Symposium on Artificial Neural Networks (pp. 325-330). Brussels: d-side publications.
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    Villmann, T., Schleif, F. - M., Kostrzewa, M., Walch, A., & Hammer, B. (2008). Classification of mass-spectrometric data in clinical proteomics using learning vector quantization methods. Briefings in Bioinformatics, 9(2), 129-143. https://doi.org/10.1093/bib/bbn009
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    Villmann, T., Hammer, B., Schleif, F. - M., Hermann, W., & Cottrell, M. (2008). Fuzzy Classification Using Information Theoretic Learning Vector Quantization. Neurocomputing, 71(16-18), 3070-3076. https://doi.org/10.1016/j.neucom.2008.04.048
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    2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2001836
    Geweniger, T., Schleif, F. - M., Hasenfuss, A., Hammer, B., & Villmann, T. (2008). Comparison of cluster algorithms for the analysis of text data using Kolmogorov complexity. In M. Köppen, N. K. Kasabov, & G. G. Coghill (Eds.), ICONIP 2008 (pp. 61-69). Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-642-03040-6_8
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    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993848 OA
    Rossi, F., Hasenfuß, A., & Hammer, B. (2007). Accelerating Relational Clustering Algorithms With Sparse Prototype Representation. Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007) Bielefeld: Bielefeld University. https://doi.org/10.2390/biecoll-wsom2007-144
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    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994016 OA
    Schneider, P., Biehl, M., Schleif, F. - M., & Hammer, B. (2007). Advanced metric adaptation in Generalized LVQ for classification of mass spectrometry data. Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007) Bielefeld: Bielefeld University. https://doi.org/10.2390/biecoll-wsom2007-135
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    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994267 OA
    Villmann, T., Schleif, F. - M., Merenyi, E., Strickert, M., & Hammer, B. (2007). Class imaging of hyperspectral satellite remote sensing data using FLSOM. Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007) Bielefeld: Bielefeld University. https://doi.org/10.2390/biecoll-wsom2007-110
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    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994295 OA
    Witoelar, A., Biehl, M., & Hammer, B. (2007). Learning Vector Quantization: generalization ability and dynamics of competing prototypes. Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007) Bielefeld: Bielefeld University. https://doi.org/10.2390/biecoll-wsom2007-126
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    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993265 OA
    Alex, N., Hammer, B., & Klawonn, F. (2007). Single pass clustering for large data sets. Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007) Bielefeld: Bielefeld University. https://doi.org/10.2390/biecoll-wsom2007-137
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    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993563 OA
    Hammer, B., Hasenfuß, A., Rossi, F., & Strickert, M. (2007). Topographic Processing of Relational Data. Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007) Bielefeld: Bielefeld University. https://doi.org/10.2390/biecoll-wsom2007-121
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    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993547
    Hammer, B., Hasenfuss, A., Schleif, F. - M., Villmann, T., Strickert, M., & Seiffert, U. (2007). Intuitive Clustering of Biological Data. Proceedings of International Joint Conference on Neural Networks, 1877-1882. IEEE. https://doi.org/10.1109/IJCNN.2007.4371244
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    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 (Vol. 4723, pp. 93-105). Berlin: Springer. https://doi.org/10.1007/978-3-540-74825-0_9
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    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.
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    2007 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993297
    Biehl, M., Ghosh, A., & Hammer, B. (2007). Dynamics and generalization ability of LVQ algorithms. Journal of Machine Learning Research, 8, 323-360.
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    2007 | Report | Veröffentlicht | PUB-ID: 1993533
    Hammer, B., & Hasenfuss, A. (2007). Relational topographic Maps (IfI Technical reports). Clausthal-Zellerfeld: Clausthal University of Technology.
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    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.
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    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 (pp. 563-570). Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-540-73400-0_72
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    Schneider, P., Biehl, M., & Hammer, B. (2007). Relevance matrices in LVQ. In M. Verleysen (Ed.), Proc. Of European Symposium on Artificial Neural Networks (pp. 37-42). Brussels, Belgium: d-side publications.
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    Schleif, F. - M., Hammer, B., & Villmann, T. (2007). Margin based Active Learning for LVQ Networks. Neurocomputing, 70(7-9), 1215-1224. https://doi.org/10.1016/j.neucom.2006.10.149
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    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.
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    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) (pp. 79-90). Brussels, Belgium: d-side publications.
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    Hasenfuss, A., Hammer, B., Schleif, F. - M., & Villmann, T. (2007). Neural gas clustering for dissimilarity data with continuous prototypes. In F. Sandoval, A. Prieto, J. Cabestany, & M. Grana (Eds.), Computational and Ambient Intelligence – Proceedings of the 9th Work-conference on Artificial Neural Networks. LNCS 4507 (pp. 539-546). Berlin: Springer. https://doi.org/10.1007/978-3-540-73007-1_66
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    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994299
    Witolaer, A., Biehl, M., Ghosh, A., & Hammer, B. (2007). On the dynamics of vector quantization and neural gas. In M. Verleysen (Ed.), Proc. Of European Symposium on Artificial Neural Networks (ESANN'2007) (pp. 127-132). Brussels, Belgium: d-side publications.
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    2007 | Konferenzband | Veröffentlicht | PUB-ID: 1994321
    Biehl M., Hammer B., Verleysen M., & Villmann T. (Eds.) (2007). Similarity-based Clustering and its Application to Medicine and Biology (7131). Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI).
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    2007 | Herausgeber*in Sammelwerk | Veröffentlicht | PUB-ID: 1994326
    Hammer B., & Hitzler P. (Eds.) (2007). Perspectives of Neural-Symbolic Integration (Studies in Computational Intelligence, 77). Berlin: Springer. https://doi.org/10.1007/978-3-540-73954-8
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    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 (pp. 190-204). Berlin: Springer. https://doi.org/10.1007/978-3-540-74565-5_16
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    2007 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993616
    Hammer, B., Hasenfuss, A., & Villmann, T. (2007). Magnification control for batch neural gas. Neurocomputing, 70(7-9), 1225-1234. https://doi.org/10.1016/j.neucom.2006.10.147
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    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.), Studies in computational Intelligence, 77. Perspectives of Neural-Symbolic Integration (pp. 67-94). Berlin: Springer. https://doi.org/10.1007/978-3-540-73954-8_4
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    Hasenfuss, A., Hammer, B., Schleif, F. - M., & Villmann, T. (2007). Neural gas clustering for sparse proximity data. In F. Sandoval, A. Prieto, J. Cabestany, & M. Grana (Eds.), Proceedings of the 9th International Work-Conference on Artificial Neural Networks.LNCS 4507 (pp. 539-546). Berlin, Heidelberg, Germany: Springer.
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    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, A. Prieto, J. Cabestany, & M. Grana (Eds.), Computational and Ambient Intelligence. Proceedings of the 9th International Work-Conference on Artificial Neural Networks. LNCS, 4507 (pp. 1036-1044). Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-540-73007-1_125
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    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.), Studies in computational Intelligence, 77. Perspectives of Neural-Symbolic Integration (pp. 95-134). Berlin: Springer. https://doi.org/10.1007/978-3-540-73954-8_5
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    Villmann, T., Schleif, F. - M., Merenyi, E., & Hammer, B. (2007). Fuzzy Labeled Self Organizing Map for Clasification of Spectra. In F. Sandoval, A. Prieto, J. Cabestany, & M. Grana (Eds.), Computational and Ambient Intelligence. Proceedings of the 9th Work-conference on Artificial Neural Networks. LNCS, 4507 (pp. 556-563). Berlin: Springer. https://doi.org/10.1007/978-3-540-73007-1_68
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    Hammer, B., Hasenfuss, A., Schleif, F. - M., & Villmann, T. (2006). Supervised Batch Neural Gas. In F. Schwenker (Ed.), Proceedings of Conference Artificial Neural Networks in Pattern Recognition (ANNPR) (pp. 33-45). Berlin: Springer Verlag. https://doi.org/10.1007/11829898_4
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    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 (pp. 539-544). Brussels, Belgium: d-side publications.
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    Villmann, T., Hammer, B., Schleif, F. - M., Geweniger, T., Fischer, T., & Cottrell, M. (2006). Prototype based classification using information theoretic learning. In I. King, J. Wang, L. Chan, & D. L. L. Wang (Eds.), Lecture Notes in Computer Science, 4233: Vol. Part II. Neural Information Processing, 13th International Conference. Proceedings (pp. 40-49). Berlin: Springer.
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    Villmann, T., Seiffert, U., Schleif, F. - M., Brüß, C., Geweniger, T., & Hammer, B. (2006). Fuzzy Labeled Self-Organizing Map with Label-Adjusted Prototypes. In F. Schwenker (Ed.), Proceedings of Conference Artificial Neural Networks in Pattern Recognition (pp. 46-56). Berlin: Springer. https://doi.org/10.1007/11829898_5
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    Schleif, F. - M., Elssner, T., Kostrzewa, M., Villmann, T., & Hammer, B. (2006). Machine Learning and Soft-Computing in Bioinformatics. A Short Journey. Proc. of FLINS 2006, 541-548. World Scientific Press.
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    Biehl, M., Hammer, B., & Schneider, P. (2006). Matrix Learning in Learning Vector Quantization. Clausthal-Zellerfeld: Clausthal University of Technology.
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    Cottrell, M., Hammer, B., Hasenfuss, A., & Villmann, T. (2006). Batch and Median Neural Gas. Neural Networks, 19(6-7), 762-771. https://doi.org/10.1016/j.neunet.2006.05.018
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    Hammer, B., Hasenfuss, A., Schleif, F. - M., & Villmann, T. (2006). Supervised median neural gas. In C. Dagli, A. Buczak, D. Enke, A. Embrechts, & O. Ersoy (Eds.), Smart Engineering System Design. Intelligent Engineering Systems Through Artificial Neural Networks, 16 (pp. 623-633). ASME Press.
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    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993594
    Hammer, B., Hasenfuss, A., Schleif, F. - M., & Villmann, T. (2006). Supervised median clustering. In C. H. Dagli (Ed.), ASME Press series on intelligent engineering systems through artificial neural networks, 16. 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) (pp. 623-632). New York, NY: ASME Press.
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    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993878
    Schleif, F. - M., Elssner, T., Kostrzewa, M., Villmann, T., & Hammer, B. (2006). Analysis and Visualization of Proteomic Data by Fuzzy labeled Self-Organizing Maps. In D. J. Lee, B. Nutter, S. Antani, S. Mitra, & J. Archibald (Eds.), 19th IEEE International Symposium on Computer- based Medical Systems (pp. 919-924). Los Alamitos: IEEE Computer Society Press. https://doi.org/10.1109/cbms.2006.44
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    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994028
    Seiffert, U., Hammer, B., Kaski, S., & Villmann, T. (2006). Neural Networks and Machine Learning in Bioinformatics - Theory and Applications. In M. Verleysen (Ed.), Proc. Of European Symposium on Artificial Neural Networks (pp. 521-532). Brussels, Belgium: d-side publications.
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    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 (pp. 141-159). Berlin: Springer. https://doi.org/10.1007/11613022_14
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    2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994237
    Villmann, T., Schleif, F. - M., & Hammer, B. (2006). Comparison of relevance learning vector quantization with other metric adaptive classification methods. Neural Networks, 19(5), 610-622. https://doi.org/10.1016/j.neunet.2005.07.013
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  • [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), 660-670. https://doi.org/10.1016/j.neucom.2005.12.007
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  • [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), 817-829. https://doi.org/10.1016/j.neunet.2006.05.010
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  • [109]
    2006 | Report | Veröffentlicht | PUB-ID: 1993584
    Hammer, B., Hasenfuss, A., Schleif, F. - M., & Villmann, T. (2006). Supervised median clustering (IfI Technical reports). Clausthal-Zellerfeld: Clausthal University of Technology.
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    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 (pp. 7-12). Brussels: d-side publications.
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    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
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    2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993762
    Hammer, B., & Villmann, T. (2006). Effizient Klassifizieren und Clustern: Lernparadigmen von Vektorquantisierern. Künstliche Intelligenz, 3(6), 5-11.
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    2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994082
    Strickert, M., Seiffert, U., Sreenivasulu, N., Weschke, W., Villmann, T., & Hammer, B. (2006). Generalized relevance LVQ (GRLVQ) with correlation measures for gene expression analysis. Neurocomputing, 69(7-9), 651-659. https://doi.org/10.1016/j.neucom.2005.12.004
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  • [104]
    2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994195
    Villmann, T., Hammer, B., Schleif, F. - M., Geweniger, T., & Herrmann, W. (2006). Fuzzy Classification by Fuzzy Labeled Neural Gas. Neural Networks, 19(6-7), 772-779. https://doi.org/10.1016/j.neunet.2006.05.026
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  • [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), 2425-2428. https://doi.org/10.1016/j.neucom.2006.02.003
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    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2017225
    Hammer, B., Villmann, T., Schleif, F. - M., Albani, C., & Hermann, W. (2006). Learning vector quantization classification with local relevance determination for medical data. In L. Rutkowski, R. Tadeusiewicz, L. A. Zadeh, & J. Zurada (Eds.), Lecture notes in computer science ; 4029 : Lecture notes in artificial intelligence: Vol. 4029. Artificial Intelligence and Soft-Computing - Proceedings of ICAISC 2006. LNAI, 4029 (pp. 603-612). Berlin, Heidelberg: Springer. https://doi.org/10.1007/11785231_63
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  • [101]
    2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982120
    Villmann, T., Schleif, F. - M., & Hammer, B. (2005). Fuzzy Labeled Soft Nearest Neighbor Classification with Relevance Learning. Fourth International Conference on Machine Learning and Applications (ICMLA'05), 11-15. IEEE. https://doi.org/10.1109/ICMLA.2005.38
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  • [100]
    2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993624
    Hammer, B., Micheli, A., Neubauer, N., Sperduti, A., & Strickert, M. (2005). Self Organizing Maps for Time Series. Proceedings of WSOM 2005, 115-122.
    PUB
     
  • [99]
    2005 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994057
    Strickert, M., & Hammer, B. (2005). Merge SOM for temporal data. Neurocomputing, 64, 39-71. https://doi.org/10.1016/j.neucom.2004.11.014
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  • [98]
    2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994172
    Villmann, T., Hammer, B., Schleif, F. - M., & Geweniger, T. (2005). Fuzzy Labeled Neural GAS for Fuzzy Classification. In M. Cottrell (Ed.), Proceedings of the 5th Workshop on Self-Organizing Maps [on CD-ROM] (pp. 283-290). Paris, France: University Paris-1-Pantheon-Sorbonne.
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    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 (pp. 13-18). Evere: d-side publishing.
    PUB
     
  • [96]
    2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993386
    Cottrell, M., Hammer, B., Hasenfuss, A., & Villmann, T. (2005). Batch NG. Proceedings of WSOM 2005, 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), 95-127. https://doi.org/10.1016/j.tcs.2005.09.008
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  • [94]
    2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993444
    Ghosh, A., Biehl, M., & Hammer, B. (2005). Dynamical Analysis of LVQ type learning rules. Proceedings of WSOM, 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), 1109-1159. https://doi.org/10.1162/0899766053491878
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    2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993665
    Hammer, B., Rechtien, A., Strickert, M., & Villmann, V. (2005). Relevance learning for mental disease classification. In M. Verleysen (Ed.), ESANN'05 (pp. 139-144). d-side publishing.
    PUB
     
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    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 (pp. 369-374). d-side publishing.
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    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. 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, 1-3. https://doi.org/10.1016/j.neucom.2004.06.008
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  • [88]
    2005 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993416
    Gersmann, K., & Hammer, B. (2005). Improving iterative repair strategies for scheduling with the SVM. Neurocomputing, 63, 271-292. https://doi.org/10.1016/j.neucom.2004.01.193
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  • [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.
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    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), 21-44. https://doi.org/10.1007/s11063-004-3255-2
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    2005 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993671
    Hammer, B., Saunders, C., & Sperduti, A. (2005). Special issue on neural networks and kernel methods for structured domains. Neural Networks, 18(8), 1015-1018. https://doi.org/10.1016/j.neunet.2005.07.004
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    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 (pp. 25-55). Berlin: Springer.
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    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 (pp. 290-296). Berlin, Heidelberg: Springer. https://doi.org/10.1007/11676935_36
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    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 (pp. 11-15). Los Angeles: IEEE Press.
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    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), 109-120. https://doi.org/10.1007/s11063-004-1547-1
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    2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993750
    Hammer, B., & Villmann, T. (2005). Classification using non standard metrics. In M. Verleysen (Ed.), ESANN'05 (pp. 303-316). Brussels: d-side publishing.
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    2005 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994063
    Strickert, M., Hammer, B., & Blohm, S. (2005). Unsupervised recursive sequences processing. Neurocomputing, 63, 69-97. https://doi.org/10.1016/j.neucom.2004.01.190
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    2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982121
    Gersmann, K., & Hammer, B. (2004). A reinforcement learning algorithm to improve scheduling search heuristics with the SVM. 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541), 3, 1811-1816. IEEE. https://doi.org/10.1109/IJCNN.2004.1380883
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    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.
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    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 (pp. 592-597). VDI Verlag.
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    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 (pp. 251-261). VDI Verlag.
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    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.
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    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 (pp. 281-292). D-side publications.
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    2004 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993649
    Hammer, B., Micheli, A., Sperduti, A., & Strickert, M. (2004). Recursive self-organizing network models. Neural Networks, 17(8-9), 1061-1085. https://doi.org/10.1016/j.neunet.2004.06.009
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    2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993702
    Hammer, B., Strickert, M., & Villmann, T. (2004). Relevance LVQ versus SVM. In L. Rutkowski, J. Siekmann, R. Tadeusiewicz, & L. A. Zadeh (Eds.), Artificial Intelligence and Softcomputing, Lecture Notes in Artificial Intelligence, 3070 (pp. 592-597). Berlin: Springer.
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    2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993419
    Gersmann, K., & Hammer, B. (2004). A reinforcement learning algorithm to improve scheduling search heuristics with the SVM. IJCNN
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    2004 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993654
    Hammer, B., Micheli, A., Sperduti, A., & Strickert, M. (2004). A general framework for unsupervised processing of structured data. Neurocomputing, 57, 3-35. https://doi.org/10.1016/j.neucom.2004.01.008
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    2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993870
    Schleif, F. - M., Clauss, U., Villmann, T., & Hammer, B. (2004). Supervised Relevance Neural Gas and Unified Maximum Separability Analysis for Classification of Mass Spectrometric Data. In M. A. Wani, K. J. Cios, & K. Hafeez (Eds.), Proceedings of the 3rd International Conference on Machine Learning and Applications (ICMLA) 2004 (pp. 374-379). Los Alamitos, CA, USA: IEEE Press.
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    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 (pp. 39-44). D-side publications.
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    2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994099
    Tino, P., & Hammer, B. (2004). On early stages of learning in connectionist models with feedback connections. Compositional Connectionism in Cognitive Science
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    2003 | Preprint | PUB-ID: 2999891 OA
    Hammer, B. (2003). Mathematical Aspects of Neural Netorks (Habilitation). Habilitationsschrift (kumulativ)
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    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), 1897-1929. https://doi.org/10.1162/08997660360675080
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    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), 389-403. https://doi.org/10.1016/S0893-6080(03)00021-2
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    2003 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2982122
    Tiňo, P., & Hammer, B. (2003). Architectural Bias in Recurrent Neural Networks: Fractal Analysis. Neural Computation, 15(8), 1931-1957. https://doi.org/10.1162/08997660360675099
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    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.
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    2003 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994108
    Tiño, P., & Hammer, B. (2003). Architectural Bias in Recurrent Neural Networks: Fractal Analysis. Neural Computation, 15(8), 1931-1957.
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    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] (pp. 47-52). Hibikino, Kitakyushu, Japan: Kyushu Institute of Technology.
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    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 (pp. 433-439). Evere: D-side publication.
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    2003 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993530
    Hammer, B., & Gersmann, K. (2003). A Note on the Universal Approximation Capability of Support Vector Machines. Neural Processing Letters, 17(1), 43-53.
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    2003 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1993487
    Hammer, B. (2003). Perspectives on learning symbolic data with connectionistic systems. In R. Kühn, R. Menzel, W. Menzel, U. Ratsch, M. M. Richter, & I. Stamatescu (Eds.), Adaptivity and Learning (pp. 141-160). Berlin: Springer.
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    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) (pp. 59-72). Brussels, Belgium: d-side.
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    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 (pp. 27-32). D-side publication.
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    2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994060
    Strickert, M., & Hammer, B. (2003). Neural Gas for Sequences. WSOM'03, 53-57.
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    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 (pp. 235-240). Evere: D-side publication.
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    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.
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    2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993349
    Bojer, T., Hammer, B., Strickert, M., & Villmann, T. (2003). Determining Relevant Input Dimensions for the Self-Organizing Map. In L. Rutkowski & J. Kacprzyk (Eds.), Neural Networks and Soft Computing (Proc. ICNNSC 2002) (pp. 388-393). Physica-Verlag.
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    2003 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993736
    Hammer, B., & Tiño, P. (2003). Recurrent Neural Networks with Small Weights Implement Definite Memory Machines. Neural Computation, 15(8), 1897-1929.
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    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.
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    2003 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994208
    Villmann, T., Merényi, E., & Hammer, B. (2003). Neural maps in remote sensing image analysis. Neural Networks, 16(3-4), 389-403.
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    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), 145-165. https://doi.org/10.1016/S1389-0417(01)00056-0
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    2002 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2982125
    Hammer, B., & Villmann, T. (2002). Generalized relevance learning vector quantization. Neural Networks, 15(8-9), 1059-1068. https://doi.org/10.1016/S0893-6080(02)00079-5
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    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 (pp. 389-394). De-side publication.
    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 (pp. 370-376). Berlin: Springer.
    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 (pp. 9-16). Berlin: Akademische Verlagsgesellschaft - infix - IOS Press.
    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 (pp. 357-368). D-side publication.
    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) (pp. 295-300). Brussels, Belgium: d-side.
    PUB
     
  • [39]
    2002 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993765
    Hammer, B., & Villmann, T. (2002). Generalized Relevance Learning Vector Quantization. Neural Networks, 15(8-9), 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., pp. 244-248). MIT Press.
    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), 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 (pp. 370-376). Berlin: Springer Verlag.
    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 (pp. 877-883). Berlin: Springer Verlag.
    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.), Lecture Notes in Computer Science: Vol. 2130. Artificial Neural Networks — ICANN 2001 (pp. 731-736). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/3-540-44668-0_102
    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.), Lecture Notes in Computer Science. Artificial Neural Networks — ICANN 2001 (pp. 677-683). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/3-540-44668-0_94
    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), 196-206. https://doi.org/10.1109/69.917560
    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), 151-157. https://doi.org/10.1016/S0167-6911(00)00086-4
    PUB | DOI
     
  • [29]
    2001 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993768
    Hammer, B., & Villmann, T. (2001). Input Pruning for Neural Gas Architectures. Proc. Of European Symposium on Artificial Neural Networks (ESANN'2001), 283-288. Brussels, Belgium: D facto publications.
    PUB
     
  • [28]
    2001 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993343
    Bojer, T., Hammer, B., Schunk, D., & Tluk von Toschanowitz, K. (2001). Relevance determination in learning vector quantization. In M. Verleysen (Ed.), ESANN'2001 (pp. 271-276). D-facto publications.
    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, 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 (pp. 731-736). Berlin: Springer.
    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, H. Yin, L. Allinson, & J. Slack (Eds.), Advances in Self-Organising Maps (pp. 173-180). London: Springer.
    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 (pp. 677-683). Berlin: Springer.
    PUB
     
  • [23]
    2001 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993510
    Hammer, B. (2001). Generalization Ability of Folding Networks. IEEE Trans. Knowl. Data Eng., 13(2), 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), 107-123. https://doi.org/10.1016/S0925-2312(99)00174-5
    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 (pp. 213-218). D-facto publications.
    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), 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 (pp. 264-278). Berlin: Springer.
    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), 62-79. https://doi.org/10.1007/PL00009845
    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, 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 (pp. 33-38). D-facto publications.
    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) (pp. 512-518). ICSC Academic Press.
    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 (pp. 255-260). D-facto publications.
    PUB
     
  • [8]
    1998 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993518
    Hammer, B. (1998). Some complexity results for perceptron networks. International Conference on artificial Neural Networks, 639-644
    PUB
     
  • [7]
    1997 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993526
    Hammer, B. (1997). Generalization of Elman Networks. Artificial Neural Networks - ICANN '97, 7th International Conference. Proceedings. Lecture Notes in Computer Science, 1327, 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
     
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