From Regression to Classification in Support Vector Machines

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/7258

Title:	From Regression to Classification in Support Vector Machines
Issue Date:	9-Oct-2013
Description:	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.
URI:	http://koha.mediu.edu.my:8181/xmlui/handle/1721
Other Identifiers:	AIM-1649 CBCL-166 http://hdl.handle.net/1721.1/7258
Appears in Collections:	MIT Items

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