An Efficient Method for Simplifying Decision Functions of Support Vector Machines(Control, Neural Networks and Learning,<Special Section>Nonlinear Theory and its Applications)
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概要
- 論文の詳細を見る
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.
- 社団法人電子情報通信学会の論文
- 2006-10-01
著者
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Nishi Tetsuo
Waseda Univ. Tokyo
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Nishi T
Kyushu Univ. Fukuoka Jpn
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Nishi T
Dept.of Computer Science And Communication Eng. Faculty Of Information Science And Electrical Eng. K
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Nishi Tetsuo
Faculty Of Science And Engineering Waseda Univ.
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GUO Jun
Department of Materials Science and Engineering, Faculty of Engineering and Resourc Science, Akita U
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Guo Jun
Department Of Computer Science And Communication Engineering Kyushu Univ.
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Guo Jun
Department Of Chemistry And Chemical Engineering Faculty Of Engineering Niigata University
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TAKAHASHI Norikazu
Department of Computer Science and Communication Engineering, Kyushu Univ.
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Nishi T
Faculty Of Science And Engineering Waseda Univ.
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Takahashi N
Department Of Computer Science And Communication Engineering Kyushu Univ.
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Nishi Tetsuo
Faculty Of Engineering Kyushu University
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Takahashi Norikazu
Department Of Computer Science And Communication Engineering Faculty Of Engineering Kyushu Universit
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Guo Jun
Department of Cardiology, First Affiliated Hospital of Jinan University
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