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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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
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