Improving support vector solutions by selecting a sequence of training subsets

Downs, T. and Wang, J. (2004). Improving support vector solutions by selecting a sequence of training subsets. In: Z. Yang, R. Everson and H. Yin, Intelligent Data Engineering and Automated Learning: IDEAL 2004. The Fifth International Intelligent Data and Automated Learning Conference (IDEAL), Exeter, (696-701). 25-27 August, 2004.


Author Downs, T.
Wang, J.
Title of paper Improving support vector solutions by selecting a sequence of training subsets
Conference name The Fifth International Intelligent Data and Automated Learning Conference (IDEAL)
Conference location Exeter
Conference dates 25-27 August, 2004
Proceedings title Intelligent Data Engineering and Automated Learning: IDEAL 2004   Check publisher's open access policy
Journal name Intelligent Daa Engineering and Automated Learning Ideal 2004, Proceedings   Check publisher's open access policy
Place of Publication Berlin
Publisher Springer
Publication Year 2004
Sub-type Fully published paper
ISBN 3-540-22881-0
ISSN 0302-9743
Editor Z. Yang
R. Everson
H. Yin
Volume 3177
Start page 696
End page 701
Total pages 6
Collection year 2004
Language eng
Abstract/Summary In this paper we demonstrate that it is possible to gradually improve the performance of support vector machine (SVM) classifiers by using a genetic algorithm to select a sequence of training subsets from the available data. Performance improvement is possible because the SVM solution generally lies some distance away from the Bayes optimal in the space of learning parameters. We illustrate performance improvements on a number of benchmark data sets.
Subjects E1
280213 Other Artificial Intelligence
700101 Application packages
Q-Index Code E1

 
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Created: Thu, 23 Aug 2007, 19:46:55 EST