An Efficient Method for Simplifying Decision Functions of Support Vector Machines

Authors:
Jun Guo;Norikazu Takahashi;Tetsuo Nishi
Affiliations:
The authors are with the Department of Computer Science and Communication Engineering, Kyushu Univ., Fukuoka-shi, 812-8581 Japan. E-Mail: guojun@kairo.csce.kyushu-u.ac.jp,;The authors are with the Department of Computer Science and Communication Engineering, Kyushu Univ., Fukuoka-shi, 812-8581 Japan. E-Mail: guojun@kairo.csce.kyushu-u.ac.jp,;The author is with the Faculty of Science and Engineering, Waseda Univ., Tokyo, 162-0072 Japan.
Venue:
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Year:
2006

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Abstract

A novel method to simplify decision functions of support vector machines (SVMs) is proposed in this paper. In our method, a decision function is determined first in a usual way by using all training samples. Next those support vectors which contribute less to the decision function are excluded from the training samples. Finally a new decision function is obtained by using the remaining samples. Experimental results show that the proposed method can effectively simplify decision functions of SVMs without reducing the generalization capability.