Training ν-Support Vector Classifiers: Theory and Algorithms
نویسندگان
چکیده
The ν-support vector machine (ν-SVM) for classification proposed by Schölkopf et al. has the advantage of using a parameter ν on controlling the number of support vectors. In this paper, we investigate the relation between ν-SVM and C-SVM in detail. We show that in general they are two different problems with the same optimal solution set. Hence we may expect that many numerical aspects on solving them are similar. However, comparing to regular C-SVM, its formulation is more complicated so up to now there are no effective methods for solving large-scale ν-SVM. We propose a decomposition method for ν-SVM which is competitive with existing methods for C-SVM. We also discuss the behavior of ν-SVM by some numerical experiments.
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