Transactions of the Institute of Systems, Control and Information Engineers
Online ISSN : 2185-811X
Print ISSN : 1342-5668
ISSN-L : 1342-5668
Fast Training of Support Vector Machines and Performance Comparison with Fuzzy Classifiers
Takuya INOUETakahiro UEOKAHisashi TAMAKIShigeo ABE
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2002 Volume 15 Issue 1 Pages 25-33

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Abstract

In this paper, we discuss fast training of a support vector machine for pattern classification by extracting training data around the class boundaries, and then compare performance of the support vector machine with that of a fuzzy classifier with ellipsoidal regions. First, we discuss the architecture and the training method of the support vector machine. Then, we discuss how to extract boundary data from the training data. Next, we summarize the architecture of the fuzzy classifier and discuss a feature extraction method using a two-layer neural network. Finally, we compare performance of the support vector machine with the fuzzy classifier combined with the two-layer neural network for several benchmark data sets and demonstrate the effectiveness of the fast training method.

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