From Regression to Classification in Support Vector Machines

dc.creator	Pontil, Massimiliano
dc.creator	Rifkin, Ryan
dc.creator	Evgeniou, Theodoros
dc.date	2004-10-20T21:04:26Z
dc.date	2004-10-20T21:04:26Z
dc.date	1998-11-01
dc.date.accessioned	2013-10-09T02:48:50Z
dc.date.available	2013-10-09T02:48:50Z
dc.date.issued	2013-10-09
dc.identifier	AIM-1649
dc.identifier	CBCL-166
dc.identifier	http://hdl.handle.net/1721.1/7258
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721
dc.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.
dc.format	807016 bytes
dc.format	194881 bytes
dc.format	application/postscript
dc.format	application/pdf
dc.language	en_US
dc.relation	AIM-1649
dc.relation	CBCL-166
dc.title	From Regression to Classification in Support Vector Machines

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