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By Peter L. Bartlett, Michael I. Jordan and Jon D. McAuliffe

Abstract

The support vector machine (SVM) has played an important role in bringing certain themes to the fore in computationally oriented statistics. However, it is important to place the SVM in context as but one member of a class of closely related algorithms for nonlinear classification. As we discuss, several of the “open problems” identified by the authors have in fact been the subject of a significant literature, a literature that may have been missed because it has been aimed not only at the SVM but at a broader family of algorithms. Keeping the broader class of algorithms in mind also helps to make clear that the SVM involves certain specific algorithmic choices, some of which have favorable consequences and others of which have unfavorable consequences—both in theory and in practice. The broader context helps to clarify the ties of the SVM to the surrounding statistical literature

Topics: 010400 STATISTICS, support vector machine (SVM), nonlinear classification
Publisher: Institute of Mathematical Statistics
Year: 2006
DOI identifier: 10.1214/088342306000000475
OAI identifier: oai:eprints.qut.edu.au:43988
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