期刊
PATTERN RECOGNITION
卷 38, 期 1, 页码 157-161出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.patcog.2004.06.001
关键词
support vector machine; training method; computational efficiency
This paper presents a four-step training method for increasing the efficiency of support vector machine (SVM). First, a SVM is initially trained by all the training samples, thereby producing a number of support vectors. Second, the support vectors, which make the hypersurface highly convoluted, are excluded from the training set. Third, the SVM is re-trained only by the remaining samples in the training set. Finally, the complexity of the trained SVM is further reduced by approximating the separation hypersurface with a subset of the support vectors. Compared to the initially trained SVM by all samples, the efficiency of the finally-trained SVM is highly improved, without system degradation. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据