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