Abstract
In this paper, an efficient support vector machine (SVM) algorithm for solving multi-class pattern recognition problems is proposed. The samples in each class are trained by one-class SVM (OCSVM), respectively. And then several sets of support vectors (SVs) are obtained, which well express the distribution of the original training samples. These SVs finally are combined into a set of training samples and trained by one-versus-one (OVO) method. The experimental results show the proposed method can reduce the time of training procedure meanwhile the classification accuracy is not reduced. Furthermore, it generates less SVs than traditional way.
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