21 Publikationen
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2024 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2988509Hinder, F., Vaquet, V., & Hammer, B., 2024. A Remark on Concept Drift for Dependent Data. In I. Miliou, N. Piatkowski, & P. Papapetrou, eds. Advances in Intelligent Data Analysis XXII. 22nd International Symposium on Intelligent Data Analysis, IDA 2024, Stockholm, Sweden, April 24–26, 2024, Proceedings, Part I. Lecture Notes in Computer Science. Cham: Springer Nature Switzerland, pp. 77-89.PUB | DOI
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2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2987572Schroeder, S., et al., 2024. Semantic Properties of Cosine Based Bias Scores for Word Embeddings. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods. Vol. 1. Setúbal, Portugal: SCITEPRESS - Science and Technology Publications, pp. 160-168.PUB | DOI
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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: 2982830Hinder, F., & Hammer, B., 2023. Feature Selection for Concept Drift Detection. In M. Verleysen, ed. ESANN 2023 Proceedings.PUB
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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 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2962746Artelt, A., et al., 2022. Contrasting Explanations for Understanding and Regularizing Model Adaptations. Neural Processing Letters, 55, p 5273–5297.PUB | PDF | DOI | Download (ext.) | WoS
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2022 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2984050Hinder, F., Vaquet, V., & Hammer, B., 2022. Suitability of Different Metric Choices for Concept Drift Detection. In T. Bouadi, E. Fromont, & E. Hüllermeier, eds. Advances in Intelligent Data Analysis XX. 20th International Symposium on Intelligent Data Analysis, IDA 2022, Rennes, France, April 20–22, 2022, Proceedings. Lecture Notes in Computer Science. Cham: Springer International Publishing, pp. 157-170.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 | 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 | Veröffentlicht | PUB-ID: 2957373Artelt, A., et al., 2021. Contrastive Explanations for Explaining Model Adaptations. In I. Rojas, G. Joya, & A. Catala, eds. Advances in Computational Intelligence. 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Virtual Event, June 16–18, 2021, Proceedings, Part I. Lecture Notes in Computer Science. Cham: Springer , pp. 101-112.PUB | DOI
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2021 | Konferenzbeitrag | Angenommen | PUB-ID: 2956774Hinder, F., & Hammer, B., Accepted. Concept Drift Segmentation via Kolmogorov Trees. 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 | PUB-ID: 2943260Schulz, A., Hinder, F., & Hammer, B., 2020. DeepView: Visualizing Classification Boundaries of Deep Neural Networks as Scatter Plots Using Discriminative Dimensionality Reduction. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, {IJCAI-20}.PUB | DOI | Download (ext.) | arXiv
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2020 | Konferenzbeitrag | PUB-ID: 2946488Hinder, F., Artelt, A., & Hammer, B., 2020. Towards non-parametric drift detection via Dynamic Adapting Window Independence Drift Detection (DAWIDD). In Proceedings of the 37th International Conference on Machine Learning.PUB | Download (ext.)
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2020 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2939517Pfannschmidt, L., et al., 2020. Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information. Neurocomputing.PUB | DOI | Download (ext.) | WoS | arXiv