From Regression to Classification in Support Vector Machines
Author(s)
Pontil, Massimiliano; Rifkin, Ryan; Evgeniou, Theodoros
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We study the relation between support vector machines (SVMs) for regression (SVMR) and SVM for classification (SVMC). We show that for a given SVMC solution there exists a SVMR solution which is equivalent for a certain choice of the parameters. In particular our result is that for $epsilon$ sufficiently close to one, the optimal hyperplane and threshold for the SVMC problem with regularization parameter C_c are equal to (1-epsilon)^{- 1} times the optimal hyperplane and threshold for SVMR with regularization parameter C_r = (1-epsilon)C_c. A direct consequence of this result is that SVMC can be seen as a special case of SVMR.
Date issued
1998-11-01Other identifiers
AIM-1649
CBCL-166
Series/Report no.
AIM-1649CBCL-166