Fast Training of Support Vector Machines by Extracting Boundary Data

Abstract

Support vector machines have gotten wide acceptance for their high generalization ability for real world applications. But the major drawback is slow training for classification problems with a large number of training data. To overcome this problem, in this paper, we discuss extracting boundary data from the training data and train the support vector… (More)
DOI: 10.1007/3-540-44668-0_44

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