PUB - Publikationen an der Universität Bielefeld

Alle markieren

• [7]
  

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

  Pfannschmidt, Lukas. 2021. Relevance learning for redundant features. Bielefeld: Universität Bielefeld.

  PUB | PDF | DOI
   
• [6]
  

  2020 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2939517

  Pfannschmidt, Lukas, Jakob, Jonathan, Hinder, Fabian, Biehl, Michael, Tino, Peter, and Hammer, Barbara. 2020. “Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information”. Neurocomputing.

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

  2020 | Preprint | Entwurf | PUB-ID: 2942271

  Pfannschmidt, Lukas, and Hammer, Barbara. Draft. “Sequential Feature Classification in the Context of Redundancies”.

  PUB | PDF | arXiv
   
• [4]
  

  2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2933893

  Pfannschmidt, Lukas, Jakob, Jonathan, Biehl, Michael, Tino, Peter, and Hammer, Barbara. 2019. “Feature Relevance Bounds for Ordinal Regression”. In Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019), ed. Michel Verleysen. Louvain-la-Neuve: i6doc.

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

  2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2935456

  Pfannschmidt, Lukas, Göpfert, Christina, Neumann, Ursula, Heider, Dominik, and Hammer, Barbara. 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 .

  PUB | PDF | DOI | arXiv
   
• [2]
  

  2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2915273

  Göpfert, Christina, Pfannschmidt, Lukas, Göpfert, Jan Philip, and Hammer, Barbara. 2018. “Interpretation of Linear Classifiers by Means of Feature Relevance Bounds”. Neurocomputing 298: 69-79.

  PUB | PDF | DOI | WoS
   
• [1]
  

  2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2908201

  Göpfert, Christina, Pfannschmidt, Lukas, and Hammer, Barbara. 2017. “Feature Relevance Bounds for Linear Classification”. In Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ed. Michele Verleysen, 187--192. Louvain-la-Neuve: Ciaco - i6doc.com.

  PUB | Dateien verfügbar | Download (ext.)