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In this paper, we propose a learning method for fast training of support vector machines (SVMs). First, we divide the two-class training samples into two sets according to the labels. Secondly, the two set one-class samples are trained by using one-class SVM (OCSVM) respectively, and we get two set support vectors (SVs). Finally, the two set SVs are combined into a set of two-class training samples...
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