25 Publikationen
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2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2981289Hinder, F., Vaquet, V., Brinkrolf, J., Hammer, B.: Model-based explanations of concept drift. Neurocomputing. : 126640 (2023).PUB | DOI | Download (ext.) | WoS
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2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982167Hinder, 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
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2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2977934Hinder, 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
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2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2966088Hinder, 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.)
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2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969460Artelt, 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
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2022 | Konferenzbeitrag | Angenommen | PUB-ID: 2964534Vaquet, 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
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2022 | Kurzbeitrag Konferenz / Poster | PUB-ID: 2962861Hinder, F., Vaquet, V., Brinkrolf, J., Artelt, A., Hammer, B.: Localization of Concept Drift: Identifying the Drifting Datapoints. (2022).PUB
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2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2962650Vaquet, 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
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2021 | Konferenzbeitrag | PUB-ID: 2959428Hinder, F., Vaquet, V., Brinkrolf, J., Hammer, B.: Fast Non-Parametric Conditional Density Estimation using Moment Trees. IEEE Computational Intelligence Magazine. (2021).PUB
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2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2960754Hinder, 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
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2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2960755Hinder, 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
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2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2962747Artelt, 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
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2021 | Konferenzbeitrag | Angenommen | PUB-ID: 2955948Brinkrolf, 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
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2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2940666Brinkrolf, 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
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2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2918254Brinkrolf, 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.)
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2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2914945Brinkrolf, 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
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2017 | Konferenzbeitrag | PUB-ID: 2914950Brinkrolf, J., Berger, K., Hammer, B.: Differential Privacy for Learning Vector Quantization. New Challenges in Neural Computation. (2017).PUB
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2016 | Konferenzbeitrag | PUB-ID: 2909365Brinkrolf, 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