25 Publikationen
-
2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2981289Hinder, F., Vaquet, V., Brinkrolf, J., & Hammer, B. (2023). Model-based explanations of concept drift. Neurocomputing, 126640. https://doi.org/10.1016/j.neucom.2023.126640PUB | DOI | Download (ext.) | WoS
-
2023 | Bielefelder E-Dissertation | PUB-ID: 2985339Brinkrolf, J. (2023). Learning Vector Quantization for the Real-World: Privacy, Robustness, and Sparsity. Bielefeld: Universität Bielefeld. https://doi.org/10.4119/unibi/2985339PUB | PDF | DOI
-
-
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 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 (pp. 164-175). Setúbal: SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0011797500003411PUB | DOI
-
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 B. Crémilleux, S. Hess, & S. Nijssen (Eds.), Lecture Notes in Computer Science: Vol. 13876. Advances in Intelligent Data Analysis XXI. 21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-la-Neuve, Belgium, April 12–14, 2023, Proceedings (pp. 182-194). Cham: Springer . https://doi.org/10.1007/978-3-031-30047-9_15PUB | DOI
-
2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2966088Hinder, F., Vaquet, V., Brinkrolf, J., Artelt, A., & Hammer, B. (2022). Localization of Concept Drift: Identifying the Drifting Datapoints. 2022 International Joint Conference on Neural Networks (IJCNN), 1-9. IEEE. https://doi.org/10.1109/IJCNN55064.2022.9892374PUB | DOI | Download (ext.)
-
2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969460Artelt, A., Brinkrolf, J., Visser, R., & Hammer, B. (2022). Explaining Reject Options of Learning Vector Quantization Classifiers. Proceedings of the 14th International Joint Conference on Computational Intelligence, 249-261. SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0011389600003332PUB | DOI
-
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. Presented at the 30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2022, Bruges.PUB
-
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. Presented at thePUB
-
2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2962650Vaquet, 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
-
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
-
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. IEEE Symposium Series on Computational Intelligence, 1-7. https://doi.org/10.1109/SSCI50451.2021.9659903PUB | DOI
-
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. 2021 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings, 01-08. Piscataway, NJ: IEEE. https://doi.org/10.1109/SSCI50451.2021.9660111PUB | DOI
-
2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2960755Hinder, F., Vaquet, V., Brinkrolf, J., & Hammer, B. (2021). Fast Non-Parametric Conditional Density Estimation using Moment Trees. 2021 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings, 1-7. Piscataway, NJ: IEEE. https://doi.org/10.1109/SSCI50451.2021.9660031PUB | DOI
-
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. 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 01-09. Piscataway, NJ: IEEE. https://doi.org/10.1109/SSCI50451.2021.9660058PUB | DOI
-
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 LearningPUB
-
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
-
-
-
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.)
-
-
2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2914945Brinkrolf, J., & Hammer, B. (2017). 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) Piscataway, NJ: IEEE. doi:10.1109/WSOM.2017.8020028PUB | DOI
-
-
2017 | Konferenzbeitrag | PUB-ID: 2914950Brinkrolf, J., Berger, K., & Hammer, B. (2017). Differential Privacy for Learning Vector Quantization. New Challenges in Neural ComputationPUB
-
2016 | Konferenzbeitrag | PUB-ID: 2909365Brinkrolf, J., Mittag, T., Joppen, R., Dr\, A., Pietsch, K. - H., & Hammer, B. (2016). Virtual optimisation for improved production planning. New Challenges in Neural ComputationPUB