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A learning algorithm for improving the classification speed of support vector machines

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3 Author(s)
Jun Guo ; Dept. of Comput. Sci. & Commun. Eng., Kyushu Univ., Fukuoka, Japan ; Takahashi, N. ; Nishi, T.

A novel method for training support vector machines (SVMs) is proposed to speed up the SVMs in test phase. It has three main steps. First, an SVM is trained on all the training samples, thereby producing a number of support vectors. Second, the support vectors, which contribute less to the shape of the decision surface, are excluded from the training set. Finally, the SVM is re-trained only on the remaining samples. Compared to the initially trained SVM, the efficiency of the finally trained SVM is highly improved, without system degradation.

Published in:

Circuit Theory and Design, 2005. Proceedings of the 2005 European Conference on  (Volume:3 )

Date of Conference:

28 Aug.-2 Sept. 2005

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