Interpretation of linear classifiers by means of feature relevance bounds

@article{Gpfert2018InterpretationOL,
  title={Interpretation of linear classifiers by means of feature relevance bounds},
  author={Christina G{\"o}pfert and Lukas Pfannschmidt and Jan Philip G{\"o}pfert and B. Hammer},
  journal={Neurocomputing},
  year={2018},
  volume={298},
  pages={69-79}
}
  • Christina Göpfert, Lukas Pfannschmidt, +1 author B. Hammer
  • Published 2018
  • Mathematics, Computer Science
  • Neurocomputing
  • Abstract Research on feature relevance and feature selection problems goes back several decades, but the importance of these areas continues to grow as more and more data becomes available, and machine learning methods are used to gain insight and interpret, rather than solely to solve classification or regression problems. Despite the fact that feature relevance is often discussed, it is frequently poorly defined, and the feature selection problems studied are subtly different. Furthermore… CONTINUE READING

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