From Regression to Classification in Support Vector Machines
Generate an AI Snapshot to get a quick, structured summary of this paper.
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
It is shown that for a given SVMC solution there exists a SVMR solution which is equivalent for a certain choice of the parameters, and SVMC can be seen as a special case of SVMR.
Abstract
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 ɛ sufficiently close to one, the optimal hyperplane and threshold for the SVMC problem with regularization parameter Cc are equal to 1 1−ɛ times the optimal hyperplane and threshold for SVMR with regularization parameter Cr =(1−ɛ)Cc. A direct consequence of this result is that SVMC can be seen as a special case of SVMR. 1