From regression to classification in support vector machines
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.