PUB - Publikationen an der Universität Bielefeld

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• [25]
  

  2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2981289

  Hinder, F., Vaquet, V., Brinkrolf, J., & Hammer, B. (2023). Model-based explanations of concept drift. Neurocomputing, 126640. https://doi.org/10.1016/j.neucom.2023.126640

  PUB | DOI | Download (ext.) | WoS
   
• [24]
  

  2023 | Bielefelder E-Dissertation | PUB-ID: 2985339

  Brinkrolf, J. (2023). Learning Vector Quantization for the Real-World: Privacy, Robustness, and Sparsity. Bielefeld: Universität Bielefeld. https://doi.org/10.4119/unibi/2985339

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• [23]
  

  2023 | Konferenzbeitrag | Angenommen | PUB-ID: 2982899

  Vaquet, V., Brinkrolf, J., & Hammer, B. (Accepted). Robust Feature Selection and Robust Training to Cope with Hyperspectral Sensor Shifts. Presented at the

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• [22]
  

  2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982167

  Hinder, F., Vaquet, V., Brinkrolf, J., & Hammer, B. (2023). On the Hardness and Necessity of Supervised Concept Drift Detection. In M. De Marsico, G. Sanniti di Baja, & A. Fred (Eds.), Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods ICPRAM. Vol. 1 (pp. 164-175). Setúbal: SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0011797500003411

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• [21]
  

  2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2977934

  Hinder, F., Vaquet, V., Brinkrolf, J., & Hammer, B. (2023). On the Change of Decision Boundary and Loss in Learning with Concept Drift. In B. Crémilleux, S. Hess, & S. Nijssen (Eds.), Lecture Notes in Computer Science: Vol. 13876. Advances in Intelligent Data Analysis XXI. 21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-la-Neuve, Belgium, April 12–14, 2023, Proceedings (pp. 182-194). Cham: Springer . https://doi.org/10.1007/978-3-031-30047-9_15

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• [20]
  

  2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2966088

  Hinder, F., Vaquet, V., Brinkrolf, J., Artelt, A., & Hammer, B. (2022). Localization of Concept Drift: Identifying the Drifting Datapoints. 2022 International Joint Conference on Neural Networks (IJCNN), 1-9. IEEE. https://doi.org/10.1109/IJCNN55064.2022.9892374

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• [19]
  

  2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969460

  Artelt, A., Brinkrolf, J., Visser, R., & Hammer, B. (2022). Explaining Reject Options of Learning Vector Quantization Classifiers. Proceedings of the 14th International Joint Conference on Computational Intelligence, 249-261. SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0011389600003332

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• [18]
  

  2022 | Konferenzbeitrag | Angenommen | PUB-ID: 2964534

  Vaquet, V., Hinder, F., Brinkrolf, J., Menz, P., Seiffert, U., & Hammer, B. (Accepted). Federated learning vector quantization for dealing with drift between nodes. Presented at the 30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2022, Bruges.

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• [17]
  

  2022 | Kurzbeitrag Konferenz / Poster | PUB-ID: 2962861

  Hinder, F., Vaquet, V., Brinkrolf, J., Artelt, A., & Hammer, B. (2022). Localization of Concept Drift: Identifying the Drifting Datapoints. Presented at the

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• [16]
  

  2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2962650

  Vaquet, V., Artelt, A., Brinkrolf, J., & Hammer, B. (2022). Taking care of our drinking water: Dealing with Sensor Faults in Water Distribution Networks. Presented at the 31st International Conference on Artificial Neural Networks, Bristol.

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• [15]
  

  2021 | Konferenzbeitrag | PUB-ID: 2959428

  Hinder, F., Vaquet, V., Brinkrolf, J., & Hammer, B. (2021). Fast Non-Parametric Conditional Density Estimation using Moment Trees. IEEE Computational Intelligence Magazine

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• [14]
  

  2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2960687

  Vaquet, V., Hinder, F., Vaquet, J., Brinkrolf, J., & Hammer, B. (2021). Online Learning on Non-Stationary Data Streams for Image Recognition using Deep Embeddings. IEEE Symposium Series on Computational Intelligence, 1-7. https://doi.org/10.1109/SSCI50451.2021.9659903

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• [13]
  

  2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2960754

  Hinder, F., Brinkrolf, J., Vaquet, V., & Hammer, B. (2021). A Shape-Based Method for Concept Drift Detection and Signal Denoising. 2021 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings, 01-08. Piscataway, NJ: IEEE. https://doi.org/10.1109/SSCI50451.2021.9660111

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• [12]
  

  2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2960755

  Hinder, F., Vaquet, V., Brinkrolf, J., & Hammer, B. (2021). Fast Non-Parametric Conditional Density Estimation using Moment Trees. 2021 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings, 1-7. Piscataway, NJ: IEEE. https://doi.org/10.1109/SSCI50451.2021.9660031

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• [11]
  

  2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2962747

  Artelt, A., Vaquet, V., Velioglu, R., Hinder, F., Brinkrolf, J., Schilling, M., & Hammer, B. (2021). Evaluating Robustness of Counterfactual Explanations. 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 01-09. Piscataway, NJ: IEEE. https://doi.org/10.1109/SSCI50451.2021.9660058

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• [10]
  

  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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• [9]
  

  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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• [8]
  

  2019 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2933715

  Brinkrolf, J., Göpfert, C., & Hammer, B. (2019). Differential privacy for learning vector quantization. Neurocomputing, 342, 125-136. doi:10.1016/j.neucom.2018.11.095

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• [7]
  

  2019 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2932914

  Brinkrolf, J., & Hammer, B. (2019). Time integration and reject options for probabilistic output of pairwise LVQ. Neural Computing and Applications. doi:10.1007/s00521-018-03966-0

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• [6]
  

  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).

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• [5]
  

  2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2918244

  Brinkrolf, J., & Hammer, B. (2018). Interpretable Machine Learning with Reject Option. at - Automatisierungstechnik, 66(4), 283-290. doi:10.1515/auto-2017-0123

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• [4]
  

  2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2914945

  Brinkrolf, J., & Hammer, B. (2017). Probabilistic extension and reject options for pairwise LVQ. 2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM) Piscataway, NJ: IEEE. doi:10.1109/WSOM.2017.8020028

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• [3]
  

  2017 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2909372

  Schulz, A., Brinkrolf, J., & Hammer, B. (2017). Efficient Kernelization of Discriminative Dimensionality Reduction. Neurocomputing, 268(SI), 34-41. doi:10.1016/j.neucom.2017.01.104

  PUB | PDF | DOI | WoS
   
• [2]
  

  2017 | Konferenzbeitrag | PUB-ID: 2914950

  Brinkrolf, J., Berger, K., & Hammer, B. (2017). Differential Privacy for Learning Vector Quantization. New Challenges in Neural Computation

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• [1]
  

  2016 | Konferenzbeitrag | PUB-ID: 2909365

  Brinkrolf, J., Mittag, T., Joppen, R., Dr\, A., Pietsch, K. - H., & Hammer, B. (2016). Virtual optimisation for improved production planning. New Challenges in Neural Computation

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