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SVM-Based Feature Selection by Direct Objective Minimisation

  • Julia Neumann
  • Christoph Schnörr
  • Gabriele Steidl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3175)

Abstract

We propose various novel embedded approaches for (simultaneous) feature selection and classification within a general optimisation framework. In particular, we include linear and nonlinear SVMs. We apply difference of convex functions programming to solve our problems and present results for artificial and real-world data.

Keywords

Support Vector Machine Feature Selection Neural Information Processing System Feature Selection Approach Feature Selection Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Julia Neumann
    • 1
  • Christoph Schnörr
    • 1
  • Gabriele Steidl
    • 1
  1. 1.Dept. of Mathematics and Computer ScienceUniversity of MannheimMannheimGermany

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