Johannes Brinkrolf

jb@techfak.uni-bielefeld.de
ORCID logo https://orcid.org/0000-0002-0032-7623

PEVZ-ID
40028560

25 Publikationen

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