PUB - Publikationen an der Universität Bielefeld

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• [7]
  

  2021 | Bielefelder E-Dissertation | PUB-ID: 2959861

  Pfannschmidt, L. (2021). Relevance learning for redundant features. Bielefeld: Universität Bielefeld. https://doi.org/10.4119/unibi/2959861

  PUB | PDF | DOI
   
• [6]
  

  2020 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2939517

  Pfannschmidt, L., Jakob, J., Hinder, F., Biehl, M., Tino, P., & Hammer, B. (2020). Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information. Neurocomputing. doi:10.1016/j.neucom.2019.12.133

  PUB | DOI | Download (ext.) | WoS | arXiv
   
• [5]
  

  2020 | Preprint | Entwurf | PUB-ID: 2942271

  Pfannschmidt, L., & Hammer, B. (Draft). Sequential Feature Classification in the Context of Redundancies

  PUB | PDF | arXiv
   
• [4]
  

  2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2933893

  Pfannschmidt, L., Jakob, J., Biehl, M., Tino, P., & Hammer, B. (2019). Feature Relevance Bounds for Ordinal Regression. In M. Verleysen (Ed.), Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019) Louvain-la-Neuve: i6doc.

  PUB | Download (ext.) | arXiv
   
• [3]
  

  2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2935456

  Pfannschmidt, L., Göpfert, C., Neumann, U., Heider, D., & Hammer, B. (2019). 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. doi:10.1109/CIBCB.2019.8791489

  PUB | PDF | DOI | arXiv
   
• [2]
  

  2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2915273

  Göpfert, C., Pfannschmidt, L., Göpfert, J. P., & Hammer, B. (2018). Interpretation of Linear Classifiers by Means of Feature Relevance Bounds. Neurocomputing, 298, 69-79. doi:10.1016/j.neucom.2017.11.074

  PUB | PDF | DOI | WoS
   
• [1]
  

  2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2908201

  Göpfert, C., Pfannschmidt, L., & Hammer, B. (2017). Feature Relevance Bounds for Linear Classification. In M. Verleysen (Ed.), Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 187--192). Louvain-la-Neuve: Ciaco - i6doc.com.

  PUB | Dateien verfügbar | Download (ext.)