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Current Feature Selection Techniques in Statistical Pattern Recognition

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Book cover Computer Recognition Systems

Part of the book series: Advances in Soft Computing ((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.

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Author information

Authors and Affiliations

  1. Dept. of Pattern Recognition, Inst. of Information Theory and Automation, Academy of Sciences of the Czech Republic, 182 08, Prague 8, Czech Republic

    Pavel Pudil & Petr Somol

Authors
  1. Pavel Pudil
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  2. Petr Somol
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Editor information

Editors and Affiliations

  1. Faculty of Electronics, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Marek Kurzyński , Edward Puchała , Michał Woźniak  & Andrzej żołnierek , ,  & 

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© 2005 Springer-Verlag Berlin Heidelberg

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Pudil, P., Somol, P. (2005). Current Feature Selection Techniques in Statistical Pattern Recognition. In: Kurzyński, M., Puchała, E., Woźniak, M., żołnierek, A. (eds) Computer Recognition Systems. Advances in Soft Computing, vol 30. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32390-2_5

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  • DOI: https://doi.org/10.1007/3-540-32390-2_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25054-8

  • Online ISBN: 978-3-540-32390-7

  • eBook Packages: EngineeringEngineering (R0)

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