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Abstract:
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Standard classification algorithms aim to minimize the probability of making an
incorrect classification. In many important applications, however, some kinds of errors
are more important than others. In this report we review cost-sensitive extensions of
standard support vector machines (SVMs). In particular, we describe cost-sensitive
extensions of the C-SVM and the nu-SVM, which we denote the 2C-SVM and 2nu-SVM
respectively. The C-SVM and the nu-SVM are known to be closely related, and we
prove that the 2C-SVM and 2nu-SVM share a similar relationship. This demonstrates
that the 2C-SVM and 2nu-SVM explore the same space of possible classifiers, and gives
us a clear understanding of the parameter space for both versions. |