PUB - Publikationen an der Universität Bielefeld

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• [7]
  

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

  Relevance learning for redundant features
  Pfannschmidt, Lukas, Relevance learning for redundant features. (). Bielefeld, 2021

  PUB | PDF | DOI
   
• [6]
  

  2020 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2939517

  Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information
  Pfannschmidt, Lukas, Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information. Neurocomputing (). , 2020

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

  2020 | Preprint | Entwurf | PUB-ID: 2942271

  Sequential Feature Classification in the Context of Redundancies
  Pfannschmidt, Lukas, Sequential Feature Classification in the Context of Redundancies. (). , 2020

  PUB | PDF | arXiv
   
• [4]
  

  2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2933893

  Feature Relevance Bounds for Ordinal Regression
  Pfannschmidt, Lukas, Feature Relevance Bounds for Ordinal Regression. Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019) (). Louvain-la-Neuve, 2019

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

  2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2935456

  FRI - Feature Relevance Intervals for Interpretable and Interactive Data Exploration
  Pfannschmidt, Lukas, FRI - Feature Relevance Intervals for Interpretable and Interactive Data Exploration. (). , 2019

  PUB | PDF | DOI | arXiv
   
• [2]
  

  2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2915273

  Interpretation of Linear Classifiers by Means of Feature Relevance Bounds
  Göpfert, Christina, Interpretation of Linear Classifiers by Means of Feature Relevance Bounds. Neurocomputing 298 (). , 2018

  PUB | PDF | DOI | WoS
   
• [1]
  

  2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2908201

  Feature Relevance Bounds for Linear Classification
  Göpfert, Christina, Feature Relevance Bounds for Linear Classification. Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (). Louvain-la-Neuve, 2017

  PUB | Dateien verfügbar | Download (ext.)