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An Efficient Method for Simplifying Decision Functions of Support Vector Machines Jun GUO Norikazu TAKAHASHI Tetsuo NISHI Publication IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences Vol.E89-A No.10 pp.2795-2802 Publication Date: 2006/10/01 Online ISSN: 1745-1337 DOI: 10.1093/ietfec/e89-a.10.2795 Print ISSN: 0916-8508 Type of Manuscript: Special Section PAPER (Special Section on Nonlinear Theory and its Applications) Category: Control, Neural Networks and Learning Keyword: support vector machines, decision function, complexity, span, Full Text: PDF(467.9KB)>>
Summary: 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. | ||||||||||
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