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Current Feature Selection Techniques in Statistical Pattern Recognition

  • Pavel Pudil
  • Petr Somol
Part of the Advances in Soft Computing book series (AINSC, volume 30)

Abstract

The paper addresses the problem of feature selection (abbreviated FS in the sequel) in statistical pattern recognition with particular emphasis to recent knowledge. Besides over-viewing advances in methodology it attempts to put them into a taxonomical framework. The methods discussed include the latest variants of the Branch & Bound algorithm, enhanced sub-optimal techniques and the simultaneous semi-parametric probability density function modeling and feature space selection method.

Keywords

Feature Selection Feature Subset Feature Selection Method Optimal Search Feature Subset Selection 
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 2005

Authors and Affiliations

  • Pavel Pudil
    • 1
  • Petr Somol
    • 1
  1. 1.Dept. of Pattern Recognition, Inst. of Information Theory and AutomationAcademy of Sciences of the Czech RepublicPrague 8Czech Republic

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