25 Publikationen
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2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2981289Hinder, F.; Vaquet, V.; Brinkrolf, J.; Hammer, B. (2023): Model-based explanations of concept drift Neurocomputing,126640PUB | DOI | Download (ext.) | WoS
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2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982167Hinder, F.; Vaquet, V.; Brinkrolf, J.; Hammer, B. (2023): On the Hardness and Necessity of Supervised Concept Drift Detection. In: Maria De Marsico; Gabriella Sanniti di Baja; Ana Fred (Hrsg.): Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods ICPRAM. Vol. 1. Setúbal: SCITEPRESS - Science and Technology Publications. S. 164-175.PUB | DOI
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2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2977934Hinder, F.; Vaquet, V.; Brinkrolf, J.; Hammer, B. (2023): On the Change of Decision Boundary and Loss in Learning with Concept Drift. In: Bruno Crémilleux; Sibylle Hess; Siegfried Nijssen (Hrsg.): Advances in Intelligent Data Analysis XXI. 21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-la-Neuve, Belgium, April 12–14, 2023, Proceedings. Cham: Springer . (Lecture Notes in Computer Science, 13876). S. 182-194.PUB | DOI
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2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2966088Hinder, 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. S. 1-9.PUB | DOI | Download (ext.)
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2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969460Artelt, 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. S. 249-261.PUB | DOI
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2022 | Konferenzbeitrag | Angenommen | PUB-ID: 2964534Vaquet, V.; Hinder, F.; Brinkrolf, J.; Menz, P.; Seiffert, U.; Hammer, B. (Accepted): Federated learning vector quantization for dealing with drift between nodes.PUB
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2022 | Kurzbeitrag Konferenz / Poster | PUB-ID: 2962861Hinder, F.; Vaquet, V.; Brinkrolf, J.; Artelt, A.; Hammer, B. (2022): Localization of Concept Drift: Identifying the Drifting Datapoints.PUB
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2021 | Konferenzbeitrag | PUB-ID: 2959428Hinder, F.; Vaquet, V.; Brinkrolf, J.; Hammer, B. (2021): Fast Non-Parametric Conditional Density Estimation using Moment Trees IEEE Computational Intelligence MagazinePUB
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2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2960687Vaquet, V.; Hinder, F.; Vaquet, J.; Brinkrolf, J.; Hammer, B. (2021): Online Learning on Non-Stationary Data Streams for Image Recognition using Deep Embeddings, In: IEEE (Hrsg.), IEEE Symposium Series on Computational Intelligence, 1-7.PUB | DOI
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2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2960754Hinder, 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. S. 01-08.PUB | DOI
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2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2960755Hinder, 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. S. 1-7.PUB | DOI
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2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2962747Artelt, 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. S. 01-09.PUB | DOI
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2021 | Konferenzbeitrag | Angenommen | PUB-ID: 2955948Brinkrolf, J.; Hammer, B. (Accepted): Federated Learning Vector Quantization. In: Michel Verleysen (Hrsg.): Proceedings of the ESANN, 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.PUB
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2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2940666Brinkrolf, J.; Hammer, B. (2020): Sparse Metric Learning in Prototype-based Classification. In: Michel Verleysen (Hrsg.): Proceedings of the ESANN, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. S. 375-380.PUB
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2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2918254Brinkrolf, J.; Berger, K.; Hammer, B. (2018): Differential private relevance learning. In: Michel Verleysen (Hrsg.): Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018). S. 555-560.PUB | Download (ext.)
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2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2914945Brinkrolf, 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
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2017 | Konferenzbeitrag | PUB-ID: 2914950Brinkrolf, J.; Berger, K.; Hammer, B. (2017): Differential Privacy for Learning Vector Quantization. In: New Challenges in Neural Computation.PUB
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2016 | Konferenzbeitrag | PUB-ID: 2909365Brinkrolf, 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