25 Publikationen

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  • [25]
    2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2981289
    Hinder, F., Vaquet, V., Brinkrolf, J., Hammer, B.: Model-based explanations of concept drift. Neurocomputing. : 126640 (2023).
    PUB | DOI | Download (ext.) | WoS
     
  • [24]
    2023 | Bielefelder E-Dissertation | PUB-ID: 2985339 OA
    Brinkrolf, J.: Learning Vector Quantization for the Real-World: Privacy, Robustness, and Sparsity. Universität Bielefeld, Bielefeld (2023).
    PUB | PDF | DOI
     
  • [23]
    2023 | Konferenzbeitrag | Angenommen | PUB-ID: 2982899 OA
    Vaquet, V., Brinkrolf, J., Hammer, B.: Robust Feature Selection and Robust Training to Cope with Hyperspectral Sensor Shifts. (Accepted).
    PUB | PDF
     
  • [22]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982167
    Hinder, F., Vaquet, V., Brinkrolf, J., Hammer, B.: On the Hardness and Necessity of Supervised Concept Drift Detection. In: De Marsico, M., Sanniti di Baja, G., and Fred, A. (eds.) Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods ICPRAM. Vol. 1. p. 164-175. SCITEPRESS - Science and Technology Publications, Setúbal (2023).
    PUB | DOI
     
  • [21]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2977934
    Hinder, F., Vaquet, V., Brinkrolf, J., Hammer, B.: On the Change of Decision Boundary and Loss in Learning with Concept Drift. In: Crémilleux, B., Hess, S., and Nijssen, S. (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. 13876, p. 182-194. Springer , Cham (2023).
    PUB | DOI
     
  • [20]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2966088
    Hinder, F., Vaquet, V., Brinkrolf, J., Artelt, A., Hammer, B.: Localization of Concept Drift: Identifying the Drifting Datapoints. 2022 International Joint Conference on Neural Networks (IJCNN). p. 1-9. IEEE (2022).
    PUB | DOI | Download (ext.)
     
  • [19]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969460
    Artelt, A., Brinkrolf, J., Visser, R., Hammer, B.: Explaining Reject Options of Learning Vector Quantization Classifiers. Proceedings of the 14th International Joint Conference on Computational Intelligence. p. 249-261. SCITEPRESS - Science and Technology Publications (2022).
    PUB | DOI
     
  • [18]
    2022 | Konferenzbeitrag | Angenommen | PUB-ID: 2964534
    Vaquet, V., Hinder, F., Brinkrolf, J., Menz, P., Seiffert, U., Hammer, B.: 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 (Accepted).
    PUB
     
  • [17]
    2022 | Kurzbeitrag Konferenz / Poster | PUB-ID: 2962861
    Hinder, F., Vaquet, V., Brinkrolf, J., Artelt, A., Hammer, B.: Localization of Concept Drift: Identifying the Drifting Datapoints. (2022).
    PUB
     
  • [16]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2962650 OA
    Vaquet, V., Artelt, A., Brinkrolf, J., Hammer, B.: 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 (2022).
    PUB | PDF
     
  • [15]
    2021 | Konferenzbeitrag | PUB-ID: 2959428
    Hinder, F., Vaquet, V., Brinkrolf, J., Hammer, B.: Fast Non-Parametric Conditional Density Estimation using Moment Trees. IEEE Computational Intelligence Magazine. (2021).
    PUB
     
  • [14]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2960687
    Vaquet, V., Hinder, F., Vaquet, J., Brinkrolf, J., Hammer, B.: Online Learning on Non-Stationary Data Streams for Image Recognition using Deep Embeddings. IEEE Symposium Series on Computational Intelligence. 1-7 (2021).
    PUB | DOI
     
  • [13]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2960754
    Hinder, F., Brinkrolf, J., Vaquet, V., Hammer, B.: A Shape-Based Method for Concept Drift Detection and Signal Denoising. 2021 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings. p. 01-08. IEEE, Piscataway, NJ (2021).
    PUB | DOI
     
  • [12]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2960755
    Hinder, F., Vaquet, V., Brinkrolf, J., Hammer, B.: Fast Non-Parametric Conditional Density Estimation using Moment Trees. 2021 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings. p. 1-7. IEEE, Piscataway, NJ (2021).
    PUB | DOI
     
  • [11]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2962747
    Artelt, A., Vaquet, V., Velioglu, R., Hinder, F., Brinkrolf, J., Schilling, M., Hammer, B.: Evaluating Robustness of Counterfactual Explanations. 2021 IEEE Symposium Series on Computational Intelligence (SSCI). p. 01-09. IEEE, Piscataway, NJ (2021).
    PUB | DOI
     
  • [10]
    2021 | Konferenzbeitrag | Angenommen | PUB-ID: 2955948
    Brinkrolf, J., Hammer, B.: Federated Learning Vector Quantization. In: Verleysen, M. (ed.) Proceedings of the ESANN, 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. (Accepted).
    PUB
     
  • [9]
    2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2940666
    Brinkrolf, J., Hammer, B.: Sparse Metric Learning in Prototype-based Classification. In: Verleysen, M. (ed.) Proceedings of the ESANN, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. p. 375-380. (2020).
    PUB
     
  • [8]
    2019 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2933715 OA
    Brinkrolf, J., Göpfert, C., Hammer, B.: Differential privacy for learning vector quantization. Neurocomputing. 342, 125-136 (2019).
    PUB | PDF | DOI | WoS
     
  • [7]
    2019 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2932914
    Brinkrolf, J., Hammer, B.: Time integration and reject options for probabilistic output of pairwise LVQ. Neural Computing and Applications. (2019).
    PUB | DOI | WoS
     
  • [6]
    2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2918254
    Brinkrolf, J., Berger, K., Hammer, B.: Differential private relevance learning. In: Verleysen, M. (ed.) Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018). p. 555-560. (2018).
    PUB | Download (ext.)
     
  • [5]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2918244
    Brinkrolf, J., Hammer, B.: Interpretable Machine Learning with Reject Option. at - Automatisierungstechnik. 66, 283-290 (2018).
    PUB | DOI | WoS
     
  • [4]
    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2914945
    Brinkrolf, J., Hammer, B.: 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). IEEE, Piscataway, NJ (2017).
    PUB | DOI
     
  • [3]
    2017 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2909372 OA
    Schulz, A., Brinkrolf, J., Hammer, B.: Efficient Kernelization of Discriminative Dimensionality Reduction. Neurocomputing. 268, 34-41 (2017).
    PUB | PDF | DOI | WoS
     
  • [2]
    2017 | Konferenzbeitrag | PUB-ID: 2914950
    Brinkrolf, J., Berger, K., Hammer, B.: Differential Privacy for Learning Vector Quantization. New Challenges in Neural Computation. (2017).
    PUB
     
  • [1]
    2016 | Konferenzbeitrag | PUB-ID: 2909365
    Brinkrolf, J., Mittag, T., Joppen, R., Dr\, A., Pietsch, K.-H., Hammer, B.: Virtual optimisation for improved production planning. New Challenges in Neural Computation. (2016).
    PUB
     

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