521 Publikationen

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  • [521]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2988175
    Ashraf, M.I., et al., 2024. Physics-Informed Graph Neural Networks for Water Distribution Systems. Presented at the 38th Annual AAAI Conference on Artificial Intelligence 2024, Vancouver.
    PUB
     
  • [520]
    2024 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2988165
    Muschalik, M., et al., 2024. Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles. Proceedings of the AAAI Conference on Artificial Intelligence, 38(13), p 14388-14396.
    PUB | DOI
     
  • [519]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2987573
    Grimmelsmann, N., et al., 2024. Predicting the Level of Co-Activation of One Muscle Head from the Other Muscle Head of the Biceps Brachii Muscle by Linear Regression and Shallow Feedforward Neural Networks. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies. SCITEPRESS - Science and Technology Publications, pp. 611-621.
    PUB | DOI
     
  • [518]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2987572
    Schroeder, S., et al., 2024. Semantic Properties of Cosine Based Bias Scores for Word Embeddings. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications, pp. 160-168.
    PUB | DOI
     
  • [517]
    2023 | Konferenzbeitrag | PUB-ID: 2987580
    Fumagalli, F., et al., 2023. SHAP-IQ: Unified Approximation of any-order Shapley Interactions. In Advances in Neural Information Processing Systems 36 (NeurIPS 2023).
    PUB | Download (ext.) | arXiv
     
  • [516]
    2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2969734 OA
    Kuhl, U., Artelt, A., & Hammer, B., 2023. Let's go to the Alien Zoo: Introducing an experimental framework to study usability of counterfactual explanations for machine learning. Frontiers in Computer Science, 5: 1087929.
    PUB | PDF | DOI | Download (ext.) | WoS | arXiv
     
  • [515]
    2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2981289
    Hinder, F., et al., 2023. Model-based explanations of concept drift. Neurocomputing, : 126640.
    PUB | DOI | Download (ext.) | WoS
     
  • [514]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2985684
    Kummert, J., et al., 2023. Generating Cardiovascular Data to Improve Training of Assistive Heart Devices. In 2023 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, pp. 1292-1297.
    PUB | DOI
     
  • [513]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2985683
    Feldhans, R., et al., 2023. Data Augmentation for Cardiovascular Time Series Data Using WaveNet. In 2023 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, pp. 836-841.
    PUB | DOI
     
  • [512]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2985571
    Artelt, A., et al., 2023. Unsupervised Unlearning of Concept Drift with Autoencoders. In 2023 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, pp. 703-710.
    PUB | DOI
     
  • [511]
    2023 | Konferenzbeitrag | Angenommen | PUB-ID: 2982899 OA
    Vaquet, V., Brinkrolf, J., & Hammer, B., Accepted. Robust Feature Selection and Robust Training to Cope with Hyperspectral Sensor Shifts.
    PUB | PDF
     
  • [510]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982830
    Hinder, F., & Hammer, B., 2023. Feature Selection for Concept Drift Detection. In M. Verleysen, ed. ESANN 2023 Proceedings.
    PUB
     
  • [509]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2983756
    Fumagalli, F., et al., 2023. On Feature Removal for Explainability in Dynamic Environments. In ESANN 2023 proceedings. pp. 83-88.
    PUB | DOI
     
  • [508]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2983943
    Muschalik, M., et al., 2023. iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams. In D. Koutra, et al., eds. Machine Learning and Knowledge Discovery in Databases: Research Track. European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part III. Lecture Notes in Computer Science. Cham: Springer Nature Switzerland, pp. 428-445.
    PUB | DOI
     
  • [507]
    2023 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2983727
    Fumagalli, F., et al., 2023. Incremental permutation feature importance (iPFI): towards online explanations on data streams. Machine Learning .
    PUB | DOI | WoS
     
  • [506]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2983942
    Muschalik, M., et al., 2023. iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios. In L. Longo, ed. Explainable Artificial Intelligence. First World Conference, xAI 2023, Lisbon, Portugal, July 26–28, 2023, Proceedings, Part I. Communications in Computer and Information Science. Cham: Springer Nature Switzerland, pp. 177-194.
    PUB | DOI
     
  • [505]
    2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2983759
    Koundouri, P., et al., 2023. Behavioral Economics and Neuroeconomics of Environmental Values. Annual Review of Resource Economics, 15(1), p 153-176.
    PUB | DOI | Download (ext.) | WoS
     
  • [504]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2984049
    Ashraf, I., et al., 2023. Spatial Graph Convolution Neural Networks for Water Distribution Systems. In B. Crémilleux, S. Hess, & S. Nijssen, eds. Advances in Intelligent Data Analysis XXI. 21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-la-Neuve, Belgium, April 12–14, 2023, Proceedings. Lecture Notes in Computer Science. Cham: Springer Nature Switzerland, pp. 29-41.
    PUB | DOI
     
  • [503]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2984048
    Schulte-Schüren, C., et al., 2023. Best of both, Structured and Unstructured Sparsity in Neural Networks. In Proceedings of the 3rd Workshop on Machine Learning and Systems. New York, NY, USA: ACM, pp. 104-108.
    PUB | DOI
     
  • [502]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2984047
    Kenneweg, P., et al., 2023. Faster Convergence for Transformer Fine-tuning with Line Search Methods. In 2023 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 1-8.
    PUB | DOI
     
  • [501]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2983795
    Kuhl, U., Artelt, A., & Hammer, B., 2023. For Better or Worse: The Impact of Counterfactual Explanations’ Directionality on User Behavior in xAI. In L. Longo, ed. Explainable Artificial Intelligence. First World Conference, xAI 2023, Lisbon, Portugal, July 26–28, 2023, Proceedings, Part III. Communications in Computer and Information Science. Cham: Springer Nature Switzerland, pp. 280-300.
    PUB | DOI
     
  • [500]
    2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2983728
    Artelt, A., Visser, R., & Hammer, B., 2023. "I do not know! but why?"- Local model-agnostic example-based explanations of reject. Neurocomputing, 558: 126722.
    PUB | DOI | WoS
     
  • [499]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2980971
    Strotherm, J., & Hammer, B., 2023. Fairness-Enhancing Ensemble Classification in Water Distribution Networks. Presented at the International Work-Conference on Artificial Neural Networks (IWANN) 2023, Ponta Delgada.
    PUB | DOI | Download (ext.)
     
  • [498]
    2023 | Preprint | Veröffentlicht | PUB-ID: 2980970
    Strotherm, J., et al., 2023. Fairness in KI-Systemen.
    PUB | Download (ext.) | arXiv
     
  • [497]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2983457
    Schroeder, S., et al., 2023. Measuring Fairness with Biased Data: A Case Study on the Effects of Unsupervised Data in Fairness Evaluation. In I. Rojas, G. Joya, & A. Catala, eds. Advances in Computational Intelligence. 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Ponta Delgada, Portugal, June 19–21, 2023, Proceedings, Part I. Lecture Notes in Computer Science. Cham: Springer Nature Switzerland, pp. 134-145.
    PUB | DOI
     
  • [496]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2983455
    Liuliakov, A., et al., 2023. One-Class Intrusion Detection with Dynamic Graphs. In L. Iliadis, et al., eds. Artificial Neural Networks and Machine Learning – ICANN 2023. 32nd International Conference on Artificial Neural Networks, Heraklion, Crete, Greece, September 26–29, 2023, Proceedings, Part IV. Lecture Notes in Computer Science. Cham: Springer Nature Switzerland, pp. 537-549.
    PUB | DOI
     
  • [495]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2983406
    Stahlhofen, P., et al., 2023. Adversarial Attacks on Leakage Detectors in Water Distribution Networks. In I. Rojas, G. Joya, & A. Catala, eds. Advances in Computational Intelligence. 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Ponta Delgada, Portugal, June 19–21, 2023, Proceedings, Part II. Lecture Notes in Computer Science. Cham: Springer Nature Switzerland, pp. 451-463.
    PUB | DOI | Preprint
     
  • [494]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2983250
    Vieth, M., Schulz, A., & Hammer, B., 2023. Extending Drift Detection Methods to Identify When Exactly the Change Happened. In I. Rojas, G. Joya, & A. Catala, eds. Advances in Computational Intelligence. 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Ponta Delgada, Portugal, June 19–21, 2023, Proceedings, Part I. Lecture Notes in Computer Science. Cham: Springer Nature Switzerland, pp. 92-104.
    PUB | DOI
     
  • [493]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982167
    Hinder, F., et al., 2023. On the Hardness and Necessity of Supervised Concept Drift Detection. In M. De Marsico, G. Sanniti di Baja, & A. Fred, eds. Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods ICPRAM. Vol. 1. Setúbal: SCITEPRESS - Science and Technology Publications, pp. 164-175.
    PUB | DOI
     
  • [492]
    2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2978162 OA
    Stallmann, D., & Hammer, B., 2023. Unsupervised Cyclic Siamese Networks Automating Cell Imagery Analysis. Algorithms, 16(4): 205.
    PUB | PDF | DOI | WoS
     
  • [491]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2977934
    Hinder, F., et al., 2023. On the Change of Decision Boundary and Loss in Learning with Concept Drift. In B. Crémilleux, S. Hess, & S. Nijssen, eds. Advances in Intelligent Data Analysis XXI. 21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-la-Neuve, Belgium, April 12–14, 2023, Proceedings. Lecture Notes in Computer Science. no.13876 Cham: Springer , pp. 182-194.
    PUB | DOI
     
  • [490]
    2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2979703
    Liuliakov, A., Hermes, L., & Hammer, B., 2023. AutoML technologies for the identification of sparse classification and outlier detection models. Applied Soft Computing, 133: 109942.
    PUB | DOI | WoS
     
  • [489]
    2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2979026
    Jakob, J., et al., 2023. Interpretable SAM-kNN Regressor for Incremental Learning on High-Dimensional Data Streams. Applied Artificial Intelligence, 37(1): 2198846.
    PUB | DOI | WoS
     
  • [488]
    2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2980429
    Kummert, J., Schulz, A., & Hammer, B., 2023. Metric Learning with Self-Adjusting Memory for Explaining Feature Drift. SN Computer Science, 4(4): 376.
    PUB | DOI
     
  • [487]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969383
    Artelt, A., Schulz, A., & Hammer, B., 2023. "Why Here and not There?": Diverse Contrasting Explanations of Dimensionality Reduction. In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods. Setúbal, Portugal: SCITEPRESS - Science and Technology Publications, pp. 27-38.
    PUB | DOI | arXiv
     
  • [486]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969381
    Schroeder, S., et al., 2023. So Can We Use Intrinsic Bias Measures or Not? In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods. Setúbal, Portugal: SCITEPRESS - Science and Technology Publications, pp. 403-410.
    PUB | DOI
     
  • [485]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969382
    Kenneweg, P., et al., 2023. Debiasing Sentence Embedders Through Contrastive Word Pairs. In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods. Setúbal, Portugal: SCITEPRESS - Science and Technology Publications, pp. 205-212.
    PUB | DOI
     
  • [484]
    2023 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2968921
    Schilling, M., et al., 2023. Modularity in Nervous Systems-a Key to Efficient Adaptivity for Deep Reinforcement Learning. Cognitive Computation.
    PUB | DOI | WoS
     
  • [483]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2987492
    Savic, D., et al., 2022. Long-Term Transitioning of Water Distribution Systems: ERC Water-Futures Project. In Proceedings - 2nd International Join Conference on Water Distribution System Analysis (WDSA)& Computing and Control in the Water Industry (CCWI). València: Editorial Universitat Politècnica de València.
    PUB | DOI
     
  • [482]
    2022 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2967683 OA
    Kenneweg, P., Stallmann, D., & Hammer, B., 2022. Novel transfer learning schemes based on Siamese networks and synthetic data. Neural Computing and Applications, 35, p 8423–8436.
    PUB | PDF | DOI | Download (ext.) | WoS | PubMed | Europe PMC
     
  • [481]
    2022 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2962746 OA
    Artelt, A., et al., 2022. Contrasting Explanations for Understanding and Regularizing Model Adaptations. Neural Processing Letters, 55, p 5273–5297.
    PUB | PDF | DOI | Download (ext.) | WoS
     
  • [480]
    2022 | Report | Veröffentlicht | PUB-ID: 2965286
    Artelt, A., et al., 2022. Faire Algorithmen und die Fairness von Erklärungen: Informatische, rechtliche und ethische Perspektiven, DuEPublico: Duisburg-Essen Publications online, University of Duisburg-Essen, Germany.
    PUB | DOI | Download (ext.)
     
  • [479]
    2022 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2964421
    Muschalik, M., et al., 2022. Agnostic Explanation of Model Change based on Feature Importance. KI - Künstliche Intelligenz.
    PUB | DOI | Download (ext.) | WoS
     
  • [478]
    2022 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2984050
    Hinder, F., Vaquet, V., & Hammer, B., 2022. Suitability of Different Metric Choices for Concept Drift Detection. In T. Bouadi, E. Fromont, & E. Hüllermeier, eds. Advances in Intelligent Data Analysis XX. 20th International Symposium on Intelligent Data Analysis, IDA 2022, Rennes, France, April 20–22, 2022, Proceedings. Lecture Notes in Computer Science. Cham: Springer International Publishing, pp. 157-170.
    PUB | DOI
     
  • [477]
    2022 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982135
    Jakob, J., Hasenjäger, M., & Hammer, B., 2022. Reject Options for Incremental Regression Scenarios. In E. Pimenidis, et al., eds. Artificial Neural Networks and Machine Learning – ICANN 2022. 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings; Part IV. Lecture Notes in Computer Science. Cham: Springer Nature Switzerland, pp. 248-259.
    PUB | DOI
     
  • [476]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2966088
    Hinder, F., et al., 2022. Localization of Concept Drift: Identifying the Drifting Datapoints. In 2022 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 1-9.
    PUB | DOI | Download (ext.)
     
  • [475]
    2022 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2969459
    Jakob, J., et al., 2022. SAM-kNN Regressor for Online Learning in Water Distribution Networks. In E. Pimenidis, et al., eds. Artificial Neural Networks and Machine Learning – ICANN 2022. 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings, Part III. Lecture Notes in Computer Science. no.13531 Cham: Springer Nature , pp. 752-762.
    PUB | DOI
     
  • [474]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969235
    Castellani, A., Schmitt, S., & Hammer, B., 2022. Stream-Based Active Learning with Verification Latency in Non-stationary Environments. In E. Pimenidis, et al., eds. Artificial Neural Networks and Machine Learning – ICANN 2022. 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings; Part IV. Lecture Notes in Computer Science. no.13532 Cham: Springer Nature Switzerland, pp. 260-272.
    PUB | DOI | Download (ext.)
     
  • [473]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969461
    Artelt, A., & Hammer, B., 2022. “Even if …” – Diverse Semifactual Explanations of Reject. In H. Ishibuchi, ed. 2022 IEEE Symposium Series on Computational Intelligence (SSCI). Piscataway, NJ: IEEE, pp. 854-859.
    PUB | DOI
     
  • [472]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969460
    Artelt, A., et al., 2022. Explaining Reject Options of Learning Vector Quantization Classifiers. In Proceedings of the 14th International Joint Conference on Computational Intelligence. SCITEPRESS - Science and Technology Publications, pp. 249-261.
    PUB | DOI
     
  • [471]
    2022 | Zeitschriftenaufsatz | PUB-ID: 2978998
    Paaßen, B., et al., 2022. Reservoir Memory Machines as Neural Computers. IEEE Transactions on Neural Networks and Learning Systems, 33(6), p 2575–2585.
    PUB | DOI | Download (ext.) | arXiv
     
  • [470]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969736
    Kuhl, U., Artelt, A., & Hammer, B., 2022. Keep Your Friends Close and Your Counterfactuals Closer: Improved Learning From Closest Rather Than Plausible Counterfactual Explanations in an Abstract Setting. In 2022 ACM Conference on Fairness, Accountability, and Transparency. New York, NY, USA: ACM, pp. 2125-2137.
    PUB | DOI | Download (ext.)
     
  • [469]
    2022 | Konferenzbeitrag | Angenommen | PUB-ID: 2964534
    Vaquet, V., et al., Accepted. Federated learning vector quantization for dealing with drift between nodes. Presented at the 30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2022, Bruges.
    PUB
     
  • [468]
    2022 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2962928
    Vaquet, V., et al., 2022. Investigating Intensity and Transversal Drift in Hyperspectral Imaging Data. Neurocomputing.
    PUB | DOI | WoS
     
  • [467]
    2022 | Kurzbeitrag Konferenz / Poster | PUB-ID: 2962861
    Hinder, F., et al., 2022. Localization of Concept Drift: Identifying the Drifting Datapoints.
    PUB
     
  • [466]
    2022 | Preprint | PUB-ID: 2962919 OA
    Artelt, A., et al., 2022. One Explanation to Rule them All — Ensemble Consistent Explanations. ArXiv:2205.08974 .
    PUB | PDF | Download (ext.) | arXiv
     
  • [465]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2962650 OA
    Vaquet, V., et al., 2022. Taking care of our drinking water: Dealing with Sensor Faults in Water Distribution Networks. Presented at the 31st International Conference on Artificial Neural Networks, Bristol.
    PUB | PDF
     
  • [464]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2966600
    Kenneweg, P., Schroeder, S., & Hammer, B., 2022. Neural Architecture Search for Sentence Classification with BERT. In ESANN 2022 proceedings. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com, pp. 417-422.
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  • [463]
    2022 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2967296
    Velioglu, R., et al., 2022. Explainable Artificial Intelligence for Improved Modeling of Processes. In H. Yin, D. Camacho, & P. Tino, eds. Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings. Lecture Notes in Computer Science. no.13756 Cham: Springer International Publishing, pp. 313-325.
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  • [462]
    2022 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2967410
    Vieth, M., et al., 2022. Efficient Sensor Selection for Individualized Prediction Based on Biosignals. In H. Yin, D. Camacho, & P. Tino, eds. Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings. Lecture Notes in Computer Science. no.13756 Cham: Springer International Publishing, pp. 326-337.
    PUB | DOI | Download (ext.)
     
  • [461]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2967096
    Kenneweg, P., et al., 2022. Intelligent Learning Rate Distribution to Reduce Catastrophic Forgetting in Transformers. In H. Yin, D. Camacho, & P. Tino, eds. Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings. Lecture Notes in Computer Science. no.13756 Cham: Springer International Publishing, pp. 252-261.
    PUB | DOI
     
  • [460]
    2022 | Report | Veröffentlicht | PUB-ID: 2965622 OA
    Hammer, B., et al., 2022. Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse von Daten mittels Maschinellen Lernens, Bielefeld: Univ. Bielefeld, Forschungsinstitut für Kognition und Robotik.
    PUB | PDF | DOI
     
  • [459]
    2022 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2964829
    Langnickel, L., et al., 2022. BERT WEAVER: Using WEight AVERaging to Enable Lifelong Learning for Transformer-based Models. arXiv.
    PUB | DOI | arXiv
     
  • [458]
    2022 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2961873
    Göpfert, J.P., Wersing, H., & Hammer, B., 2022. Interpretable locally adaptive nearest neighbors. Neurocomputing, 470, p 344-351.
    PUB | DOI | WoS
     
  • [457]
    2021 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982165
    Liuliakov, A., & Hammer, B., 2021. AutoML Technologies for the Identification of Sparse Models. In H. Yin, et al., eds. Intelligent Data Engineering and Automated Learning – IDEAL 2021. 22nd International Conference, IDEAL 2021, Manchester, UK, November 25–27, 2021, Proceedings. Lecture Notes in Computer Science. no.13113 Cham: Springer , pp. 65-75.
    PUB | DOI
     
  • [456]
    2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2949334 OA
    Rohlfing, K., et al., 2021. Explanation as a social practice: Toward a conceptual framework for the social design of AI systems. IEEE Transactions on Cognitive and Developmental Systems, 13(3), p 717--728.
    PUB | PDF | DOI | WoS
     
  • [455]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982136
    Jakob, J., Hasenjäger, M., & Hammer, B., 2021. On the suitability of incremental learning for regression tasks in exoskeleton control. In 2021 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, pp. 1-8.
    PUB | DOI
     
  • [454]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982134
    Castellani, A., Schmitt, S., & Hammer, B., 2021. Task-Sensitive Concept Drift Detector with Constraint Embedding. In 2021 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, pp. 01-08.
    PUB | DOI
     
  • [453]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969237
    Castellani, A., Schmitt, S., & Hammer, B., 2021. Estimating the Electrical Power Output of Industrial Devices with End-to-End Time-Series Classification in the Presence of Label Noise. In N. Oliver, et al., eds. Machine Learning and Knowledge Discovery in Databases. Research Track. European Conference, ECML PKDD 2021, Bilbao, Spain, September 13–17, 2021, Proceedings, Part I. Lecture Notes in Computer Science. no.12975 Cham: Springer International Publishing, pp. 469-484.
    PUB | DOI | Download (ext.)
     
  • [452]
    2021 | Konferenzbeitrag | PUB-ID: 2959428
    Hinder, F., et al., 2021. Fast Non-Parametric Conditional Density Estimation using Moment Trees. IEEE Computational Intelligence Magazine.
    PUB
     
  • [451]
    2021 | Preprint | PUB-ID: 2959899
    Artelt, A., & Hammer, B., 2021. Convex optimization for actionable & plausible counterfactual explanations. arXiv: 2105.07630v1.
    PUB | Download (ext.) | arXiv
     
  • [450]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2960687
    Vaquet, V., et al., 2021. Online Learning on Non-Stationary Data Streams for Image Recognition using Deep Embeddings. IEEE Symposium Series on Computational Intelligence, , p 1-7.
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    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2960754
    Hinder, F., et al., 2021. A Shape-Based Method for Concept Drift Detection and Signal Denoising. In 2021 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings. Piscataway, NJ: IEEE, pp. 01-08.
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    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2960755
    Hinder, F., et al., 2021. Fast Non-Parametric Conditional Density Estimation using Moment Trees. In 2021 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings. Piscataway, NJ: IEEE, pp. 1-7.
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    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2960685
    Vaquet, V., et al., 2021. Investigating Intensity and Transversal Drift in Hyperspectral Imaging Data. In M. Verleysen, ed. ESANN 2021 proceedings. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com, pp. 47-52.
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    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2957588
    Artelt, A., & Hammer, B., 2021. Efficient computation of contrastive explanations. In 2021 International Joint Conference on Neural Networks (IJCNN). New York: Institute of Electrical and Electronics Engineers (IEEE), pp. 1-9.
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    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2957373
    Artelt, A., et al., 2021. Contrastive Explanations for Explaining Model Adaptations. In I. Rojas, G. Joya, & A. Catala, eds. Advances in Computational Intelligence. 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Virtual Event, June 16–18, 2021, Proceedings, Part I. Lecture Notes in Computer Science. Cham: Springer , pp. 101-112.
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    2021 | Report | Veröffentlicht | PUB-ID: 2954239
    Szczuka, J., et al., 2021. Können Kinder aufgeklärte Nutzer* innen von Sprachassistenten sein? Rechtliche, psychologische, ethische und informatische Perspektiven, Essen: Universität Duisburg-Essen, Universitätsbibliothek.
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    2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2957340
    Artelt, A., & Hammer, B., 2021. Efficient computation of counterfactual explanations and counterfactual metrics of prototype-based classifiers. Neurocomputing, 470(VSI: ESANN 2020), p 304-317.
    PUB | DOI | Download (ext.) | WoS
     
  • [442]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2962747
    Artelt, A., et al., 2021. Evaluating Robustness of Counterfactual Explanations. In 2021 IEEE Symposium Series on Computational Intelligence (SSCI). Piscataway, NJ: IEEE, pp. 01-09.
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  • [441]
    2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2954542
    Paaßen, B., Schulz, A., & Hammer, B., 2021. Reservoir Stack Machines. Neurocomputing, 470, p 352-364.
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  • [440]
    2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2959418
    Göpfert, J.P., et al., 2021. Intuitiveness in Active Teaching. IEEE Transactions on Human-Machine Systems, , p 1-10.
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  • [439]
    2021 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2956229
    Paassen, B., et al., 2021. Reservoir Memory Machines as Neural Computers. IEEE Transactions on Neural Networks and Learning Systems, , p 1-11.
    PUB | DOI | Download (ext.) | WoS | PubMed | Europe PMC | arXiv
     
  • [438]
    2021 | Zeitschriftenaufsatz | Angenommen | PUB-ID: 2955245
    Stallmann, D., et al., Accepted. Towards an automatic analysis of CHO-K1 suspension growth in microfluidic single-cell cultivation. Bioinformatics .
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  • [437]
    2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2958662
    Schilling, M., et al., 2021. Decentralized control and local information for robust and adaptive decentralized Deep Reinforcement Learning. Neural Networks, 144, p 699-725.
    PUB | DOI | Download (ext.) | WoS | PubMed | Europe PMC
     
  • [436]
    2021 | Konferenzbeitrag | PUB-ID: 2958664
    Hermes, L., Hammer, B., & Schilling, M., 2021. Application of Graph Convolutions in a Lightweight Model for Skeletal Human Motion Forecasting. In ESANN 2021 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. . pp. 111-116.
    PUB | arXiv
     
  • [435]
    2021 | Konferenzbeitrag | Angenommen | PUB-ID: 2956774
    Hinder, F., & Hammer, B., Accepted. Concept Drift Segmentation via Kolmogorov Trees. In M. Verleysen, ed. Proceedings of the ESANN, 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.
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  • [434]
    2021 | Konferenzbeitrag | Angenommen | PUB-ID: 2955948
    Brinkrolf, J., & Hammer, B., Accepted. Federated Learning Vector Quantization. In M. Verleysen, ed. Proceedings of the ESANN, 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.
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  • [433]
    2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2952937 OA
    Kummert, J., et al., 2021. Efficient Reject Options for Particle Filter Object Tracking in Medical Applications. Sensors, 21(6): 2114.
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  • [432]
    2021 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2955115
    Straat, M., et al., 2021. Supervised learning in the presence of concept drift: a modelling framework. Neural Computing and Applications.
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  • [431]
    2020 | Konferenzbeitrag | PUB-ID: 2943260
    Schulz, A., Hinder, F., & Hammer, B., 2020. DeepView: Visualizing Classification Boundaries of Deep Neural Networks as Scatter Plots Using Discriminative Dimensionality Reduction. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, {IJCAI-20}.
    PUB | DOI | Download (ext.) | arXiv
     
  • [430]
    2020 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982081
    Biehl, M., et al., 2020. Prototype-Based Classifiers in the Presence of Concept Drift: A Modelling Framework. In A. Vellido, et al., eds. Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization. Proceedings of the 13th International Workshop, WSOM+ 2019, Barcelona, Spain, June 26-28, 2019. Advances in Intelligent Systems and Computing. Cham: Springer International Publishing, pp. 210-221.
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  • [429]
    2020 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2958328
    Vaquet, V., & Hammer, B., 2020. Balanced SAM-kNN: Online Learning with Heterogeneous Drift and Imbalanced Data. In I. Farkaš, P. Masulli, & S. Wermter, eds. Artificial Neural Networks and Machine Learning – ICANN 2020. 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part II. Lecture Notes in Computer Science. no. 12397 Cham: Springer, pp. 850-862.
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  • [428]
    2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2957814
    Krämer, N., et al., 2020. Improving and Evaluating Conversational User Interfaces for Children. In IUI '20: Proceedings of the 25th International Conference on Intelligent User Interfaces. New York: Association for Computing Machinery.
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  • [427]
    2020 | Konferenzbeitrag | PUB-ID: 2946488
    Hinder, F., Artelt, A., & Hammer, B., 2020. Towards non-parametric drift detection via Dynamic Adapting Window Independence Drift Detection (DAWIDD). In Proceedings of the 37th International Conference on Machine Learning.
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  • [426]
    2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2946685
    Artelt, A., & Hammer, B., 2020. Efficient computation of counterfactual explanations of LVQ models. In M. Verleysen, ed. ESANN 2020 - proceedings. Louvain-la-Neuve: Ciaco , pp. 19-24.
    PUB | Download (ext.)
     
  • [425]
    2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2946761
    Artelt, A., & Hammer, B., 2020. Convex Density Constraints for Computing Plausible Counterfactual Explanations. In I. Farkas, P. Masulli, & S. Wermter, eds. Artificial Neural Networks and Machine Learning - ICANN 2020 - 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15-18, 2020, Proceedings, Part {I}. Lecture Notes in Computer Science. no.12396 Cham: Springer, pp. 353-365.
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  • [424]
    2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2940666
    Brinkrolf, J., & Hammer, B., 2020. Sparse Metric Learning in Prototype-based Classification. In M. Verleysen, ed. Proceedings of the ESANN, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. pp. 375-380.
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  • [423]
    2020 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2939517
    Pfannschmidt, L., et al., 2020. Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information. Neurocomputing.
    PUB | DOI | Download (ext.) | WoS | arXiv
     
  • [422]
    2020 | Report | Veröffentlicht | PUB-ID: 2946614 OA
    Hammer, B., et al., 2020. Sustainability and Trust for Artificial Intelligence Technologies,
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  • [421]
    2020 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2942892
    Iliadis, L.S., Kurkova, V., & Hammer, B., 2020. Brain-inspired computing and machine learning. NEURAL COMPUTING & APPLICATIONS.
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  • [420]
    2020 | Preprint | Entwurf | PUB-ID: 2942271 OA
    Pfannschmidt, L., & Hammer, B., Draft. Sequential Feature Classification in the Context of Redundancies.
    PUB | PDF | arXiv
     
  • [419]
    2019 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982085
    Göpfert, J.P., Wersing, H., & Hammer, B., 2019. Recovering Localized Adversarial Attacks. In I. V. Tetko, et al., eds. Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation. 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part I. Lecture Notes in Computer Science. Cham: Springer International Publishing, pp. 302-311.
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    2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982084
    Losing, V., et al., 2019. Personalized Online Learning of Whole-Body Motion Classes using Multiple Inertial Measurement Units. In 2019 International Conference on Robotics and Automation (ICRA). IEEE, pp. 9530-9536.
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  • [417]
    2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982082
    Hosseini, B., & Hammer, B., 2019. Large-Margin Multiple Kernel Learning for Discriminative Features Selection and Representation Learning. In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 1-8.
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  • [416]
    2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982083
    Li, P., Niggemann, O., & Hammer, B., 2019. On the Identification of Decision Boundaries for Anomaly Detection in CPPS. In 2019 IEEE International Conference on Industrial Technology (ICIT). IEEE, pp. 1311-1316.
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  • [415]
    2019 | Preprint | PUB-ID: 2959898
    Artelt, A., & Hammer, B., 2019. On the computation of counterfactual explanations - A survey. arXiv: 1911.07749v1.
    PUB | Download (ext.) | arXiv
     
  • [414]
    2019 | Monographie | PUB-ID: 2935200 OA
    Paaßen, B., Artelt, A., & Hammer, B., 2019. Lecture Notes on Applied Optimization, Faculty of Technology, Bielefeld University.
    PUB | Dateien verfügbar
     
  • [413]
    2019 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2934458 OA
    Prahm, C., et al., 2019. Counteracting Electrode Shifts in Upper-Limb Prosthesis Control via Transfer Learning. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(5), p 956-962.
    PUB | PDF | DOI | WoS | PubMed | Europe PMC
     
  • [412]
    2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2933893
    Pfannschmidt, L., et al., 2019. Feature Relevance Bounds for Ordinal Regression. In M. Verleysen, ed. Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019). Louvain-la-Neuve: i6doc.
    PUB | Download (ext.) | arXiv
     
  • [411]
    2019 | Konferenzbeitrag | Angenommen | PUB-ID: 2937842 OA
    Hosseini, B., & Hammer, B., Accepted. Deep-Aligned Convolutional Neural Network for Skeleton-based Action Recognition and Segmentation. Presented at the 2019 IEEE International Conference on Data Mining (ICDM), Beijing.
    PUB | Datei | arXiv
     
  • [410]
    2019 | Konferenzbeitrag | Angenommen | PUB-ID: 2937841 OA
    Hosseini, B., & Hammer, B., Accepted. Interpretable Multiple-Kernel Prototype Learning for Discriminative Representation and Feature Selection. Presented at the The 28th ACM International Conference on Information and Knowledge Management (CIKM) , Beijing.
    PUB | Datei | arXiv
     
  • [409]
    2019 | Report | Veröffentlicht | PUB-ID: 2937888
    Krämer, N., et al., 2019. KI-basierte Sprachassistenten im Alltag: Forschungsbedarf aus informatischer, psychologischer, ethischer und rechtlicher Sicht, Universität Duisburg-Essen, Universitätsbibliothek.
    PUB | DOI | Download (ext.)
     
  • [408]
    2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2937839 OA
    Hosseini, B., & Hammer, B., 2019. Interpretable Discriminative Dimensionality Reduction and Feature Selection on the Manifold. Presented at the 2019 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML), Würzburg.
    PUB | Datei | arXiv
     
  • [407]
    2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2935456 OA
    Pfannschmidt, L., et al., 2019. FRI - Feature Relevance Intervals for Interpretable and Interactive Data Exploration. Presented at the 16th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, Certosa di Pontignano, Siena - Tuscany, Italy.
    PUB | PDF | DOI | arXiv
     
  • [406]
    2019 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2933715 OA
    Brinkrolf, J., Göpfert, C., & Hammer, B., 2019. Differential privacy for learning vector quantization. Neurocomputing, 342, p 125-136.
    PUB | PDF | DOI | WoS
     
  • [405]
    2019 | Konferenzbeitrag | PUB-ID: 2930303
    Hosseini, B., & Hammer, B., 2019. Multiple-Kernel Dictionary Learning for Reconstruction and Clustering of Unseen Multivariate Time-series. In M. Verleysen, ed. Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019).
    PUB | arXiv
     
  • [404]
    2019 | Konferenzbeitrag | PUB-ID: 2934192
    Hosseini, B., & Hammer, B., 2019. Large-Margin Multiple Kernel Learning for Discriminative Features Selection and Representation Learning. Presented at the The 2019 International Joint Conference on Neural Networks (IJCNN), Budapest.
    PUB | arXiv
     
  • [403]
    2019 | Preprint | Veröffentlicht | PUB-ID: 2934181
    Göpfert, J.P., Wersing, H., & Hammer, B., 2019. Adversarial attacks hidden in plain sight.
    PUB | DOI | arXiv
     
  • [402]
    2019 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2932914
    Brinkrolf, J., & Hammer, B., 2019. Time integration and reject options for probabilistic output of pairwise LVQ. Neural Computing and Applications.
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  • [401]
    2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982092
    Queisser, J.F., et al., 2018. Skill Memories for Parameterized Dynamic Action Primitives on the Pneumatically Driven Humanoid Robot Child Affetto. In 2018 Joint IEEE 8th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob). IEEE, pp. 39-45.
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    2018 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982090
    Hosseini, B., & Hammer, B., 2018. Non-negative Local Sparse Coding for Subspace Clustering. In W. Duivesteijn, A. Siebes, & A. Ukkonen, eds. Advances in Intelligent Data Analysis XVII. 17th International Symposium, IDA 2018, ’s-Hertogenbosch, The Netherlands, October 24–26, 2018, Proceedings. Lecture Notes in Computer Science. Cham: Springer International Publishing, pp. 137-150.
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    2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982089
    Specht, F., et al., 2018. Generation of Adversarial Examples to Prevent Misclassification of Deep Neural Network based Condition Monitoring Systems for Cyber-Physical Production Systems. In 2018 IEEE 16th International Conference on Industrial Informatics (INDIN). IEEE, pp. 760-765.
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  • [398]
    2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982088
    Losing, V., Wersing, H., & Hammer, B., 2018. Enhancing Very Fast Decision Trees with Local Split-Time Predictions. In 2018 IEEE International Conference on Data Mining (ICDM). IEEE, pp. 287-296.
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    2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982087
    Hosseini, B., & Hammer, B., 2018. Confident Kernel Sparse Coding and Dictionary Learning. In 2018 IEEE International Conference on Data Mining (ICDM). IEEE, pp. 1031-1036.
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    2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982086
    Li, P., Niggemann, O., & Hammer, B., 2018. A Geometric Approach to Clustering Based Anomaly Detection for Industrial Applications. In IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society. IEEE, pp. 5345-5352.
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    2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2931283 OA
    Queißer, J., et al., 2018. Skill Memories for Parameterized Dynamic Action Primitives on the Pneumatically Driven Humanoid Robot Child Affetto. Presented at the International Conference on Development and Learning and on Epigenetic Robotics 2018 (ICDL-EPIROB2018), Tokyo .
    PUB | PDF
     
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    2018 | Datenpublikation | PUB-ID: 2930611 OA
    Hülsmann, F., et al., 2018. Classification of motor errors to provide real-time feedback for sports coaching in virtual reality - A case study in squats and Tai Chi pushes (Data), Bielefeld University.
    PUB | Dateien verfügbar | DOI
     
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    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2930862
    Hülsmann, F., et al., 2018. Classification of motor errors to provide real-time feedback for sports coaching in virtual reality — A case study in squats and Tai Chi pushes. Computers & Graphics, 76, p 47-59.
    PUB | DOI | Download (ext.) | WoS
     
  • [392]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2932412
    Straat, M., et al., 2018. Statistical Mechanics of On-Line Learning Under Concept Drift. ENTROPY, 20(10): 775.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [391]
    2018 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2917896
    Lux, M., et al., 2018. flowLearn: Fast and precise identification and quality checking of cell populations in flow cytometry. Bioinformatics, 34(13), p 2245-2253.
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    2018 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2933557
    Meyer, S., et al., 2018. Inferring Temporal Structure from Predictability in Bumblebee Learning Flight. In H. Yin, et al., eds. Intelligent Data Engineering and Automated Learning – IDEAL 2018. Lecture Notes in Computer Science. no.11314 Cham: Springer International Publishing, pp. 508-519.
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    2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2918254
    Brinkrolf, J., Berger, K., & Hammer, B., 2018. Differential private relevance learning. In M. Verleysen, ed. Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018). pp. 555-560.
    PUB | Download (ext.)
     
  • [388]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2911900
    Paaßen, B., Göpfert, C., & Hammer, B., 2018. Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces. Neural Processing Letters, 48(2), p 669-689.
    PUB | DOI | Download (ext.) | WoS | arXiv
     
  • [387]
    2018 | Preprint | Veröffentlicht | PUB-ID: 2921209 OA
    Hosseini, B., & Hammer, B., 2018. Non-Negative Local Sparse Coding for Subspace Clustering. Advances in Intelligent Data Analysis XVII. IDA 2018.
    PUB | Datei | Download (ext.) | arXiv
     
  • [386]
    2018 | Konferenzbeitrag | Im Druck | PUB-ID: 2932116 OA
    Hosseini, B., & Hammer, B., In Press. Confident Kernel Sparse Coding and Dictionary Learning. In 2018 IEEE International Conference on Data Mining (ICDM).
    PUB | Datei | arXiv
     
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    2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2919598
    Hosseini, B., & Hammer, B., 2018. Feasibility Based Large Margin Nearest Neighbor Metric Learning. In ESANN 2018. Proceedings of 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. pp. 219-224.
    PUB | arXiv
     
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    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2914505
    Paaßen, B., et al., 2018. Expectation maximization transfer learning and its application for bionic hand prostheses. Neurocomputing, 298, p 122-133.
    PUB | DOI | Download (ext.) | WoS | arXiv
     
  • [383]
    2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2921316 OA
    Göpfert, J.P., Hammer, B., & Wersing, H., 2018. Mitigating Concept Drift via Rejection. In V. Kurkova, et al., eds. Artificial Neural Networks and Machine Learning – ICANN 2018. Proceedings, Part I. Lecture Notes in Computer Science. no.11139 Cham: Springer.
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    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2917201
    Losing, V., Hammer, B., & Wersing, H., 2018. Tackling heterogeneous concept drift with the Self-Adjusting Memory (SAM). KNOWLEDGE AND INFORMATION SYSTEMS, 54(1), p 171-201.
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  • [381]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2915273 OA
    Göpfert, C., et al., 2018. Interpretation of Linear Classifiers by Means of Feature Relevance Bounds. Neurocomputing, 298, p 69-79.
    PUB | PDF | DOI | WoS
     
  • [380]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2913389
    Paaßen, B., et al., 2018. The Continuous Hint Factory - Providing Hints in Vast and Sparsely Populated Edit Distance Spaces. Journal of Educational Data Mining, 10(1), p 1-35.
    PUB | Download (ext.) | arXiv
     
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    2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2919844
    Paaßen, B., et al., 2018. Tree Edit Distance Learning via Adaptive Symbol Embeddings. In J. Dy & A. Krause, eds. Proceedings of the 35th International Conference on Machine Learning (ICML 2018). Proceedings of Machine Learning Research. no.80 pp. 3973-3982.
    PUB | Download (ext.) | arXiv
     
  • [378]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2914730 OA
    Losing, V., Hammer, B., & Wersing, H., 2018. Incremental on-line learning: A review and comparison of state of the art algorithms. Neurocomputing, 275, p 1261-1274.
    PUB | PDF | DOI | WoS
     
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    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2918244
    Brinkrolf, J., & Hammer, B., 2018. Interpretable Machine Learning with Reject Option. at - Automatisierungstechnik, 66(4), p 283-290.
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    2018 | Konferenzbeitrag | PUB-ID: 2916318
    Berger, K., et al., 2018. Linear Supervised Transfer Learning for the Large Margin Nearest Neighbor Classifier. Presented at the SSCI CIDM 2017
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  • [375]
    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982095
    Frenay, B., & Hammer, B., 2017. Label-noise-tolerant classification for streaming data. In 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 1748-1755.
    PUB | DOI
     
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    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982091
    Losing, V., Hammer, B., & Wersing, H., 2017. Personalized maneuver prediction at intersections. In 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC). IEEE, pp. 1-6.
    PUB | DOI
     
  • [373]
    2017 | Kurzbeitrag Konferenz / Poster | PUB-ID: 2919987 OA
    Hosseini, B., & Hammer, B., 2017. Non-negative Kernel Sparse Coding Frameworks for Efficient Analysis of Motion Data. Presented at the BMVA Symposium on Human Activity Recognition and Monitoring, London.
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    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909369 OA
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    2017 | Konferenzbeitrag | PUB-ID: 2909371
    Biehl, M., Hammer, B., & Villmann, T., 2017. Prototype based models for the supervised learning of classificaton schemes. In Proc. of the IAU Symposium 325 on Astroinformatics, Sorrento/Italy, October 2016. pp. in press.
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    2017 | Konferenzbeitrag | PUB-ID: 2914734 OA
    Losing, V., Hammer, B., & Wersing, H., 2017. Self-Adjusting Memory: How to Deal with Diverse Drift Types. Presented at the International Joint Conference on Artificial Intelligence (IJCAI) 2017, Melbourne.
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    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2908201 OA
    Göpfert, C., Pfannschmidt, L., & Hammer, B., 2017. Feature Relevance Bounds for Linear Classification. In M. Verleysen, ed. Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Louvain-la-Neuve: Ciaco - i6doc.com, pp. 187--192.
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    2017 | Konferenzbeitrag | PUB-ID: 2914732 OA
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    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2913752 OA
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    2017 | Kurzbeitrag Konferenz / Poster | PUB-ID: 2919990 OA
    Hosseini, B., & Hammer, B., 2017. Task-Driven Sparse Coding for Classification of Motion Data. Presented at the Ninth Mittweida Workshop on Computational Intelligence (MiWoCI 2017), Mittweida.
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    2017 | Konferenzbeitrag | PUB-ID: 2909370
    Frenay, B., & Hammer, B., 2017. Label-Noise-Tolerant Classification for Streaming Data. In IEEE International Joint Conference on Neural Neworks.
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    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2914141 OA
    Aswolinskiy, W., & Hammer, B., 2017. Unsupervised Transfer Learning for Time Series via Self-Predictive Modelling - First Results. In Proceedings of the Workshop on New Challenges in Neural Computation (NC2). Machine Learning Reports. no.03/2017 Bielefeld: Universität Bielefeld, CITEC.
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    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909037 OA
    Prahm, C., et al., 2017. Echo State Networks as Novel Approach for Low-Cost Myoelectric Control. In A. ten Telje, et al., eds. Proceedings of the 16th Conference on Artificial Intelligence in Medicine (AIME 2017). Lecture Notes in Computer Science. no.10259 Springer, pp. 338--342.
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    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2915274 OA
    Göpfert, C., Göpfert, J.P., & Hammer, B., 2017. Analyzing Feature Relevance for Linear Reject Option SVM using Relevance Intervals. In Proceedings of the 2017 NIPS workshop on Transparent and Interpretable Machine Learning in Safety Critical Environments.
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    2016 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2982097
    Biehl, M., Hammer, B., & Villmann, T., 2016. Prototype-based Models for the Supervised Learning of Classification Schemes. Proceedings of the International Astronomical Union, 12(S325), p 129-138.
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    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982096
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    2016 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2907633 OA
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    2016 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2783224 OA
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    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2904509
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    2016 | Konferenzbeitrag | E-Veröff. vor dem Druck | PUB-ID: 2904909 OA
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    Paaßen, B., Mokbel, B., & Hammer, B., 2015. A Toolbox for Adaptive Sequence Dissimilarity Measures for Intelligent Tutoring Systems. In O. C. Santos, et al., eds. Proceedings of the 8th International Conference on Educational Data Mining. International Educational Datamining Society, pp. 632-632.
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    2014 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982100
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    Biehl, M., Hammer, B., & Villmann, T., 2014. Distance Measures for Prototype Based Classification. In L. Grandinetti, T. Lippert, & N. Petkov, eds. Brain-Inspired Computing. International Workshop, BrainComp 2013, Cetraro, Italy, July 8-11, 2013, Revised Selected Papers. Lecture Notes in Computer Science. Cham: Springer International Publishing, pp. 100-116.
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    Gross, S., et al., 2014. How to Select an Example? A Comparison of Selection Strategies in Example-Based Learning. In S. Trausan-Matu, et al., eds. Intelligent Tutoring Systems. Lecture Notes in Computer Science. no.8474 Springer, pp. 340-347.
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    Schleif, F.-M., Hammer, B., & Villmann, T., 2007. Supervised Neural Gas for Functional Data and its Application to the Analysis of Clinical Proteom Spectra. In F. Sandoval, et al., eds. Computational and Ambient Intelligence. Proceedings of the 9th International Work-Conference on Artificial Neural Networks. LNCS, 4507. Berlin, Heidelberg: Springer, pp. 1036-1044.
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    Hammer, B., et al., 2006. Supervised Batch Neural Gas. In F. Schwenker, ed. Proceedings of Conference Artificial Neural Networks in Pattern Recognition (ANNPR). Berlin: Springer Verlag, pp. 33-45.
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    Hammer, B., & Neubauer, N., 2006. On the capacity of unsupervised recursive neural networks for symbol processing. In A. d'Avila Garcez, P. Hitzler, & G. Tamburrini, eds. Workshop proceedings of NeSy'06.
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    Hammer, B., et al., 2006. Learning vector quantization classification with local relevance determination for medical data. In L. Rutkowski, et al., eds. Artificial Intelligence and Soft-Computing - Proceedings of ICAISC 2006. LNAI, 4029. Lecture notes in computer science ; 4029 : Lecture notes in artificial intelligence. no.4029 Berlin, Heidelberg: Springer, pp. 603-612.
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    Villmann, T., Schleif, F.-M., & Hammer, B., 2005. Fuzzy Labeled Soft Nearest Neighbor Classification with Relevance Learning. In Fourth International Conference on Machine Learning and Applications (ICMLA'05). IEEE, pp. 11-15.
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    Hammer, B., Schleif, F.-M., & Villmann, T., 2005. On the Generalization Ability of Prototype-Based Classifiers with Local Relevance Determination, IfI Technical reports, Clausthal-Zellerfeld: Clausthal University of Technology.
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    Hammer, B., Strickert, M., & Villmann, T., 2005. Prototype based recognition of splice sites. In U. Seiffert, L. C. Jain, & P. Schweitzer, eds. Bioinformatics using computational intelligence paradigms. Berlin: Springer, pp. 25-55.
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    Gersmann, K., & Hammer, B., 2004. A reinforcement learning algorithm to improve scheduling search heuristics with the SVM. In 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541). no.3 IEEE, pp. 1811-1816.
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    Hammer, B., Tino, P., & Micheli, A., 2004. A mathematical characterization of the architectural bias of recursive models, Osnabrücker Schriften zur Mathematik, Osnabrück: Universität Osnabrück.
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    Tluk von Toschanowitz, K., Hammer, B., & Ritter, H., 2004. Mapping the Design Space of Reinforcement Learning Problems - a Case Study. In H. - M. Gross, K. Debes, & H. - J. Böhme, eds. SOAVE 2004, 3rd Workshop on SelfOrganization of AdaptiVE Behavior. VDI Verlag, pp. 251-261.
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    Hammer, B., & Jain, B.J., 2004. Neural methods for non-standard data. In M. Verleysen, ed. European Symposium on Artificial Neural Networks'2004. D-side publications, pp. 281-292.
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    Hammer, B., Strickert, M., & Villmann, T., 2004. Relevance LVQ versus SVM. In L. Rutkowski, et al., eds. Artificial Intelligence and Softcomputing, Lecture Notes in Artificial Intelligence, 3070. Berlin: Springer, pp. 592-597.
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    Tino, P., & Hammer, B., 2004. On early stages of learning in connectionist models with feedback connections. In Compositional Connectionism in Cognitive Science.
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    Schleif, F.-M., et al., 2004. Supervised Relevance Neural Gas and Unified Maximum Separability Analysis for Classification of Mass Spectrometric Data. In M. A. Wani, K. J. Cios, & K. Hafeez, eds. Proceedings of the 3rd International Conference on Machine Learning and Applications (ICMLA) 2004. Los Alamitos, CA, USA: IEEE Press, pp. 374-379.
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    Strickert, M., & Hammer, B., 2004. Self-organizing context learning. In M. Verleysen, ed. European Symposium on Artificial Neural Networks. D-side publications, pp. 39-44.
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    Hammer, B., & Tiňo, P., 2003. Recurrent Neural Networks with Small Weights Implement Definite Memory Machines. Neural Computation, 15(8), p 1897-1929.
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    Villmann, T., Merényi, E., & Hammer, B., 2003. Neural maps in remote sensing image analysis. Neural Networks, 16(3-4), p 389-403.
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    Tiňo, P., & Hammer, B., 2003. Architectural Bias in Recurrent Neural Networks: Fractal Analysis. Neural Computation, 15(8), p 1931-1957.
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    Tiño, P., & Hammer, B., 2003. Architectural Bias in Recurrent Neural Networks: Fractal Analysis. Neural Computation, 15(8), p 1931-1957.
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    Villmann, T., Schleif, F.-M., & Hammer, B., 2003. Supervised Neural Gas and Relevance Learning in Learning Vector Quantization. In T. Yamakawa, ed. Proceedings of the 4th Workshop on Self Organizing Maps [on CD-ROM]. Hibikino, Kitakyushu, Japan: Kyushu Institute of Technology, pp. 47-52.
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    Hammer, B., Strickert, M., & Villmann, T., 2003. On the generalization ability of GRLVQ, Osnabrücker Schriften zur Mathematik, Osnabrück: Universität Osnabrück.
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    Bojer, T., Hammer, B., & Koeers, C., 2003. Monitoring technical systems with prototype based clustering. In M. Verleysen, ed. ESANN 2003, 10th European Symposium on Artificial Neural Network. Proceedings. Evere: D-side publication, pp. 433-439.
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    Hammer, B., 2003. Perspectives on learning symbolic data with connectionistic systems. In R. Kühn, et al., eds. Adaptivity and Learning. Berlin: Springer, pp. 141-160.
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    2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993754
    Hammer, B., & Villmann, T., 2003. Mathematical Aspects of Neural Networks. In M. Verleysen, ed. Proc. Of European Symposium on Artificial Neural Networks (ESANN'2003). Brussels, Belgium: d-side, pp. 59-72.
    PUB
     
  • [53]
    2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994053
    Strickert, M., & Hammer, B., 2003. Unsupervised recursive sequence processing. In M. Verleysen, ed. 10th European Symposium on Artificial Neural Networks. Proceedings. D-side publication, pp. 27-32.
    PUB
     
  • [52]
    2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994060
    Strickert, M., & Hammer, B., 2003. Neural Gas for Sequences. In WSOM'03. pp. 53-57.
    PUB
     
  • [51]
    2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993412
    Gersmann, K., & Hammer, B., 2003. Improving iterative repair strategies for scheduling with the SVM. In M. Verleysen, ed. ESANN 2003, 10th European Symposium on Artificial Neural Networks. Proceedings. Evere: D-side publication, pp. 235-240.
    PUB
     
  • [50]
    2003 | Report | Veröffentlicht | PUB-ID: 1993645
    Hammer, B., Micheli, a., & Sperduti, A., 2003. A general framework for self-organizing structure processing neural networks, Pisa: Universita di Pisa, Dipartimento die Informatica.
    PUB
     
  • [49]
    2003 | Report | Veröffentlicht | PUB-ID: 1994157
    Villmann, T., & Hammer, B., 2003. Metric adaptation and relevance learning in learning vector quantization, Osnabrücker Schriften zur Mathematik, Osnabrück: Universität Osnabrück.
    PUB
     
  • [48]
    2003 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994208
    Villmann, T., Merényi, E., & Hammer, B., 2003. Neural maps in remote sensing image analysis. Neural Networks, 16(3-4), p 389-403.
    PUB
     
  • [47]
    2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993349
    Bojer, T., et al., 2003. Determining Relevant Input Dimensions for the Self-Organizing Map. In L. Rutkowski & J. Kacprzyk, eds. Neural Networks and Soft Computing (Proc. ICNNSC 2002). Physica-Verlag, pp. 388-393.
    PUB
     
  • [46]
    2003 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993736
    Hammer, B., & Tiño, P., 2003. Recurrent Neural Networks with Small Weights Implement Definite Memory Machines. Neural Computation, 15(8), p 1897-1929.
    PUB
     
  • [45]
    2002 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2982126
    Hammer, B., 2002. Recurrent networks for structured data – A unifying approach and its properties. Cognitive Systems Research, 3(2), p 145-165.
    PUB | DOI
     
  • [44]
    2002 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2982125
    Hammer, B., & Villmann, T., 2002. Generalized relevance learning vector quantization. Neural Networks, 15(8-9), p 1059-1068.
    PUB | DOI
     
  • [43]
    2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994146
    Villmann, T., & Hammer, B., 2002. Supervised Neural Gas for Learning Vector Quantization. In D. Polani, J. Kim, & T. Martinetz, eds. Proc. of the 5th German Workshop on Artificial Life. Berlin: Akademische Verlagsgesellschaft - infix - IOS Press, pp. 9-16.
    PUB
     
  • [42]
    2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993636
    Hammer, B., Micheli, A., & Sperduti, A., 2002. A general framework for unsupervised processing of structured data. In M. Verleysen, ed. ESANN 2002, 10th European Symposium on Artificial Neural Network. Proceedings. De-side publication, pp. 389-394.
    PUB
     
  • [41]
    2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994095
    Tino, P., & Hammer, B., 2002. Architectural bias in recurrent neural networks – fractal analysis. In J. R. Dorronsoro, ed. Proc. International Conf. on Artificial Neural Networks. Lecture Notes in Computer Science, 2415. Berlin: Springer, pp. 370-376.
    PUB
     
  • [40]
    2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993688
    Hammer, B., & Steil, J.J., 2002. Perspectives on Learning with Recurrent Neural Networks. In M. Verleysen, ed. Proc. European Symposium Artificial Neural Networks. D-side publication, pp. 357-368.
    PUB
     
  • [39]
    2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993758
    Hammer, B., & Villmann, T., 2002. Batch-GRLVQ. In M. Verleysen, ed. Proc. Of European Symposium on Artificial Neural Networks (ESANN'2002). Brussels, Belgium: d-side, pp. 295-300.
    PUB
     
  • [38]
    2002 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993765
    Hammer, B., & Villmann, T., 2002. Generalized Relevance Learning Vector Quantization. Neural Networks, 15(8-9), p 1059-1068.
    PUB
     
  • [37]
    2002 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1993471
    Hammer, B., 2002. Compositionality in Neural Systems. In M. Arbib, ed. Handbook of Brain Theory and Neural Networks. 2nd. MIT Press, pp. 244-248.
    PUB
     
  • [36]
    2002 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993508
    Hammer, B., 2002. Recurrent neural networks for structured data – a unifying approach and its properties. Cognitive Systems Research, 3(2), p 145-165.
    PUB
     
  • [35]
    2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993692
    Hammer, B., Strickert, M., & Villmann, T., 2002. Learning Vector Quantization for Multimodal Data. In J. R. Dorronsoro, ed. Proc. International Conf. on Artificial Neural Networks (ICANN). Lecture Notes in Computer Science, 2415. Berlin: Springer Verlag, pp. 370-376.
    PUB
     
  • [34]
    2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993697
    Hammer, B., Strickert, M., & Villmann, T., 2002. Rule Extraction from Self-Organizing Networks. In J. R. Dorronsoro, ed. Proc. International Conf. on Artificial Neural Networks (ICANN). Lecture Notes in Computer Science, 2415. Berlin: Springer Verlag, pp. 877-883.
    PUB
     
  • [33]
    2002 | Report | Veröffentlicht | PUB-ID: 1993729
    Hammer, B., & Tino, P., 2002. Neural networks with small weights implement finite memory machines, Osnabrücker Schriften zur Mathematik, Osnabrück: Universität Osnabrück.
    PUB
     
  • [32]
    2001 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982130
    Hammer, B., 2001. On the Generalization Ability of Recurrent Networks. In G. Dorffner, H. Bischof, & K. Hornik, eds. Artificial Neural Networks — ICANN 2001. Lecture Notes in Computer Science. no. 2130 Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 731-736.
    PUB | DOI
     
  • [31]
    2001 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982129
    Strickert, M., Bojer, T., & Hammer, B., 2001. Generalized Relevance LVQ for Time Series. In G. Dorffner, H. Bischof, & K. Hornik, eds. Artificial Neural Networks — ICANN 2001. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 677-683.
    PUB | DOI
     
  • [30]
    2001 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2982128
    Hammer, B., 2001. Generalization ability of folding networks. IEEE Transactions on Knowledge and Data Engineering, 13(2), p 196-206.
    PUB | DOI
     
  • [29]
    2001 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2982127
    Vidyasagar, M., Balaji, S., & Hammer, B., 2001. Closure properties of uniform convergence of empirical means and PAC learnability under a family of probability measures. Systems & Control Letters, 42(2), p 151-157.
    PUB | DOI
     
  • [28]
    2001 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993768
    Hammer, B., & Villmann, T., 2001. Input Pruning for Neural Gas Architectures. In Proc. Of European Symposium on Artificial Neural Networks (ESANN'2001). Brussels, Belgium: D facto publications, pp. 283-288.
    PUB
     
  • [27]
    2001 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993343
    Bojer, T., et al., 2001. Relevance determination in learning vector quantization. In M. Verleysen, ed. ESANN'2001. D-facto publications, pp. 271-276.
    PUB
     
  • [26]
    2001 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994123
    Vidyasagar, M., Balaji, S., & Hammer, B., 2001. Closure properties of uniform convergence of empirical means and PAC learnability under a family of probability measures. System and Control Letters, 42, p 151-157.
    PUB
     
  • [25]
    2001 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993474
    Hammer, B., 2001. On the Generalization Ability of Recurrent Networks. In G. Dorffner, H. Bischof, & K. Hornik, eds. Artificial Neural Networks. Proceedings. Lecture Notes in Computer Science, 2130. Berlin: Springer, pp. 731-736.
    PUB
     
  • [24]
    2001 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993739
    Hammer, B., & Villmann, T., 2001. Estimating Relevant Input Dimensions for Self-Organizing Algorithms. In N. M. Allinson, et al., eds. Advances in Self-Organising Maps. London: Springer, pp. 173-180.
    PUB
     
  • [23]
    2001 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994042
    Strickert, M., Bojer, T., & Hammer, B., 2001. Generalized Relevance LVQ for Time Series. In G. Dorffner, H. Bischof, & K. Hornik, eds. Artificial Neural Networks. International Conference. Proceedings. Lecture Notes in Computer Science, 2130. Berlin: Springer, pp. 677-683.
    PUB
     
  • [22]
    2001 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993510
    Hammer, B., 2001. Generalization Ability of Folding Networks. IEEE Trans. Knowl. Data Eng., 13(2), p 196-206.
    PUB
     
  • [21]
    2000 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2982131
    Hammer, B., 2000. On the approximation capability of recurrent neural networks. Neurocomputing, 31(1-4), p 107-123.
    PUB | DOI
     
  • [20]
    2000 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993499
    Hammer, B., 2000. Limitations of hybrid systems. In M. Verleysen, ed. European Symposium on Artificial Neural Networks. D-facto publications, pp. 213-218.
    PUB
     
  • [19]
    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
     
  • [18]
    2000 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993512
    Hammer, B., 2000. On the approximation capability of recurrent neural networks. Neurocomputing, 31(1-4), p 107-123.
    PUB
     
  • [17]
    2000 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993400
    DasGupta, B., & Hammer, B., 2000. On Approximate Learning by Multi-layered Feedforward Circuits. In H. Arimura, S. Jain, & A. Sharma, eds. Algorithmic Learning Theory, 11th International Conference. Proceedings. Lecture Notes in Computer Science, 1968. Berlin: Springer, pp. 264-278.
    PUB
     
  • [16]
    2000 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993479
    Hammer, B., 2000. Approximation and generalization issues of recurrent networks dealing with structured data. In P. Frasconi, A. Sperduti, & M. Gori, eds. ECAI workshop: Foundations of connectionist-symbolic integration: representation, paradigms, and algorithms.
    PUB
     
  • [15]
    2000 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993495
    Hammer, B., 2000. Neural networks classifying symbolic data. In L. de Raedt & S. Kramer, eds. ICML workshop on attribute-value and relational learning: crossing the boundaries. pp. 61-65.
    PUB
     
  • [14]
    1999 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2982132
    Hammer, B., 1999. On the Learnability of Recursive Data. Mathematics of Control, Signals, and Systems, 12(1), p 62-79.
    PUB | DOI
     
  • [13]
    1999 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993516
    Hammer, B., 1999. On the learnability of recursive data. Mathematics of Control, Signals and Systems, 12, p 62-79.
    PUB
     
  • [12]
    1999 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993502
    Hammer, B., 1999. Approximation capabilities of folding networks. In M. Verleysen, ed. European Symposium on Artificial Neural Networks. D-facto publications, pp. 33-38.
    PUB
     
  • [11]
    1999 | Report | Veröffentlicht | PUB-ID: 1993409
    DasGupta, B., & Hammer, B., 1999. Hardness of approximation of the loading problem for multi-layered feedforward neural networks, DIMACS Center, Rutgers University.
    PUB
     
  • [10]
    1998 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993484
    Hammer, B., 1998. On the Approximation Capability of Recurrent Neural Networks. In M. Heiss, ed. Proceedings of the International ICSC / IFAC Symposium on Neural Computation (NC 1998). ICSC Academic Press, pp. 512-518.
    PUB
     
  • [9]
    1998 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993505
    Hammer, B., 1998. Training a sigmoidal network is difficult. In M. Verleysen, ed. European Symposium on Artificial Neural Networks. D-facto publications, pp. 255-260.
    PUB
     
  • [8]
    1998 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993518
    Hammer, B., 1998. Some complexity results for perceptron networks. In International Conference on artificial Neural Networks. pp. 639-644.
    PUB
     
  • [7]
    1997 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993526
    Hammer, B., 1997. Generalization of Elman Networks. In Artificial Neural Networks - ICANN '97, 7th International Conference. Proceedings. Lecture Notes in Computer Science, 1327. Berlin: Springer, pp. 409-414.
    PUB
     
  • [6]
    1997 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993684
    Hammer, B., & Sperschneider, V., 1997. Neural networks can approximate mappings on structured objects. In P. P. Wang, ed. International conference on Computational Intelligence and Neural Networks. pp. 211-214.
    PUB
     
  • [5]
    1997 | Report | Veröffentlicht | PUB-ID: 1993524
    Hammer, B., 1997. On the generalization ability of simple recurrent neural networks, Osnabrücker Schriften zur Mathematik, Osnabrück: Universität Osnabrück.
    PUB
     
  • [4]
    1997 | Report | Veröffentlicht | PUB-ID: 1993520
    Hammer, B., 1997. Learning recursive data is intractable, Osnabrücker Schriften zur Mathematik, Osnabrück: Universität Osnabrück.
    PUB
     
  • [3]
    1997 | Report | Veröffentlicht | PUB-ID: 1993522
    Hammer, B., 1997. A NP-hardness result for a sigmoidal 3-node neural network, Osnabrücker Schriften zur Mathematik, Osnabrück: Universität Osnabrück.
    PUB
     
  • [2]
    1996 | Report | Veröffentlicht | PUB-ID: 1993528
    Hammer, B., 1996. Universal approximation of mappings on structured objects using the folding architecture, Osnabrücker Schriften zur Mathematik, Osnabrück: Universität Osnabrück.
    PUB
     
  • [1]
    1996 | Monographie | Veröffentlicht | PUB-ID: 1994039
    Sperschneider, V., & Hammer, B., 1996. Theoretische Informatik. Eine problemorientierte Einführung, erlin: Springer.
    PUB
     

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