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
    Hinder F, Vaquet V, Brinkrolf J, Hammer B (2023)
    Model-based explanations of concept drift.
    Neurocomputing: 126640.
    PUB | DOI | Download (ext.) | WoS
     
  • [24]
    2023 | Bielefelder E-Dissertation | PUB-ID: 2985339 OA
    Brinkrolf J (2023)
    Learning Vector Quantization for the Real-World: Privacy, Robustness, and Sparsity.
    Bielefeld: Universität Bielefeld.
    PUB | PDF | DOI
     
  • [23]
    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
     
  • [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: Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods ICPRAM. Vol. 1. De Marsico M, Sanniti di Baja G, Fred A (Eds); Setúbal: SCITEPRESS - Science and Technology Publications: 164-175.
    PUB | DOI
     
  • [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: Advances in Intelligent Data Analysis XXI. 21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-la-Neuve, Belgium, April 12–14, 2023, Proceedings. Crémilleux B, Hess S, Nijssen S (Eds); Lecture Notes in Computer Science, 13876. Cham: Springer : 182-194.
    PUB | DOI
     
  • [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.
    In: 2022 International Joint Conference on Neural Networks (IJCNN). IEEE: 1-9.
    PUB | DOI | Download (ext.)
     
  • [19]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969460
    Artelt A, Brinkrolf J, Visser R, Hammer B (2022)
    Explaining Reject Options of Learning Vector Quantization Classifiers.
    In: Proceedings of the 14th International Joint Conference on Computational Intelligence. SCITEPRESS - Science and Technology Publications: 249-261.
    PUB | DOI
     
  • [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.
    PUB
     
  • [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.
    PUB
     
  • [16]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2962650 OA
    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.
    PUB | PDF
     
  • [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.
    PUB
     
  • [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.
    PUB | DOI
     
  • [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.
    In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings. Piscataway, NJ: IEEE: 01-08.
    PUB | DOI
     
  • [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.
    In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings. Piscataway, NJ: IEEE: 1-7.
    PUB | DOI
     
  • [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.
    In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI). Piscataway, NJ: IEEE: 01-09.
    PUB | DOI
     
  • [10]
    2021 | Konferenzbeitrag | Angenommen | PUB-ID: 2955948
    Brinkrolf J, Hammer B (Accepted)
    Federated Learning Vector Quantization.
    In: Proceedings of the ESANN, 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed);.
    PUB
     
  • [9]
    2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2940666
    Brinkrolf J, Hammer B (2020)
    Sparse Metric Learning in Prototype-based Classification.
    In: Proceedings of the ESANN, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); 375-380.
    PUB
     
  • [8]
    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: 125-136.
    PUB | PDF | DOI | WoS
     
  • [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.
    PUB | DOI | WoS
     
  • [6]
    2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2918254
    Brinkrolf J, Berger K, Hammer B (2018)
    Differential private relevance learning.
    In: Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018). Verleysen M (Ed); 555-560.
    PUB | Download (ext.)
     
  • [5]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2918244
    Brinkrolf J, Hammer B (2018)
    Interpretable Machine Learning with Reject Option.
    at - Automatisierungstechnik 66(4): 283-290.
    PUB | DOI | WoS
     
  • [4]
    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2914945
    Brinkrolf J, Hammer B (2017)
    Probabilistic extension and reject options for pairwise LVQ.
    In: 2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM). Piscataway, NJ: IEEE.
    PUB | DOI
     
  • [3]
    2017 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2909372 OA
    Schulz A, Brinkrolf J, Hammer B (2017)
    Efficient Kernelization of Discriminative Dimensionality Reduction.
    Neurocomputing 268(SI): 34-41.
    PUB | PDF | DOI | WoS
     
  • [2]
    2017 | Konferenzbeitrag | PUB-ID: 2914950
    Brinkrolf J, Berger K, Hammer B (2017)
    Differential Privacy for Learning Vector Quantization.
    In: New Challenges in Neural Computation. .
    PUB
     
  • [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.
    In: New Challenges in Neural Computation. .
    PUB
     
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