Variable Selection for Support Vector Machines

DOI10.1080/03610910903072391zbMath1191.62112OpenAlexW2170045179MaRDI QIDQ3652711

S. J. Steel, Surette Bierman

Publication date: 16 December 2009

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610910903072391

zbMATH Keywords

kernel target alignmentrecursive feature eliminationkernel model dimensionvariable selection for SVMs

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Monte Carlo methods (65C05) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (1)

Variable Selection for Kernel Classification

Uses Software

• UCI-ml

Cites Work

• Benchmarking least squares support vector machine classifiers
• Variable selection in kernel Fisher discriminant analysis by means of recursive feature elimina\-tion
• Wrappers for feature subset selection
• 10.1162/153244303322753616
• 10.1162/153244303322753706
• 10.1162/153244303322753733
• 10.1162/153244303322753751
• On the Optimality of the Backward Greedy Algorithm for the Subset Selection Problem
• Regularization and Variable Selection Via the Elastic Net
• Choosing multiple parameters for support vector machines
• Training invariant support vector machines

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