Journal
PATTERN RECOGNITION
Volume 38, Issue 1, Pages 157-161Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.patcog.2004.06.001
Keywords
support vector machine; training method; computational efficiency
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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.
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