Lukas Pfannschmidt
lpfannschmidt@techfak.uni-bielefeld.de
PEVZ-ID
7 Publikationen
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2020 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2939517Pfannschmidt, L., Jakob, J., Hinder, F., Biehl, M., Tino, P., Hammer, B.: Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information. Neurocomputing. (2020).PUB | DOI | Download (ext.) | WoS | arXiv
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2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2933893Pfannschmidt, L., Jakob, J., Biehl, M., Tino, P., Hammer, B.: Feature Relevance Bounds for Ordinal Regression. In: Verleysen, M. (ed.) Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019). i6doc, Louvain-la-Neuve (2019).PUB | Download (ext.) | arXiv
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2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2935456Pfannschmidt, L., Göpfert, C., Neumann, U., Heider, D., Hammer, B.: FRI - Feature Relevance Intervals for Interpretable and Interactive Data Exploration. Presented at the 16th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, Certosa di Pontignano, Siena - Tuscany, Italy (2019).PUB | PDF | DOI | arXiv
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2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2908201Göpfert, C., Pfannschmidt, L., Hammer, B.: Feature Relevance Bounds for Linear Classification. In: Verleysen, M. (ed.) Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. p. 187--192. Ciaco - i6doc.com, Louvain-la-Neuve (2017).PUB | Dateien verfügbar | Download (ext.)