25 Publikationen
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2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2981289Hinder, F., et al., 2023. Model-based explanations of concept drift. Neurocomputing, : 126640.PUB | DOI | Download (ext.) | WoS
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2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982167Hinder, F., et al., 2023. On the Hardness and Necessity of Supervised Concept Drift Detection. In M. De Marsico, G. Sanniti di Baja, & A. Fred, eds. Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods ICPRAM. Vol. 1. Setúbal: SCITEPRESS - Science and Technology Publications, pp. 164-175.PUB | DOI
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2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2977934Hinder, F., et al., 2023. On the Change of Decision Boundary and Loss in Learning with Concept Drift. In B. Crémilleux, S. Hess, & S. Nijssen, 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. no.13876 Cham: Springer , pp. 182-194.PUB | DOI
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2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2966088Hinder, F., et al., 2022. Localization of Concept Drift: Identifying the Drifting Datapoints. In 2022 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 1-9.PUB | DOI | Download (ext.)
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2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969460Artelt, A., et al., 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, pp. 249-261.PUB | DOI
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2022 | Konferenzbeitrag | Angenommen | PUB-ID: 2964534Vaquet, V., et al., 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
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2022 | Kurzbeitrag Konferenz / Poster | PUB-ID: 2962861Hinder, F., et al., 2022. Localization of Concept Drift: Identifying the Drifting Datapoints.PUB
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2021 | Konferenzbeitrag | PUB-ID: 2959428Hinder, F., et al., 2021. Fast Non-Parametric Conditional Density Estimation using Moment Trees. IEEE Computational Intelligence Magazine.PUB
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2021 | Konferenzbeitrag | Angenommen | PUB-ID: 2955948Brinkrolf, J., & Hammer, B., Accepted. Federated Learning Vector Quantization. In M. Verleysen, ed. 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 M. Verleysen, ed. Proceedings of the ESANN, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. pp. 375-380.PUB
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2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2918254Brinkrolf, J., Berger, K., & Hammer, B., 2018. Differential private relevance learning. In M. Verleysen, ed. Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018). pp. 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., et al., 2016. Virtual optimisation for improved production planning. In New Challenges in Neural Computation.PUB