Training a support vector machine in the primal

Most literature on support vector machines (SVMs) concentrates on the dual optimization problem. In this letter, we point out that the primal problem can also be solved efficiently for both linear and nonlinear SVMs and that there is no reason for ignoring this possibility. On the contrary, from the...

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Veröffentlicht in:	Neural computation Jg. 19; H. 5; S. 1155
1. Verfasser:	Chapelle, Olivier
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States 01.05.2007
Schlagworte:	Algorithms Models, Theoretical Neural Networks (Computer) Nonlinear Dynamics Pattern Recognition, Automated
ISSN:	0899-7667
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Beschreibung
Zusammenfassung:	Most literature on support vector machines (SVMs) concentrates on the dual optimization problem. In this letter, we point out that the primal problem can also be solved efficiently for both linear and nonlinear SVMs and that there is no reason for ignoring this possibility. On the contrary, from the primal point of view, new families of algorithms for large-scale SVM training can be investigated.
Bibliographie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0899-7667
DOI:	10.1162/neco.2007.19.5.1155