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A Quadratic Loss Multi-Class SVM for which a Radius–Margin Bound Applies
Volume 22, Issue 1 (2011), pp. 73–96
Yann Guermeur   Emmanuel Monfrini  

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https://doi.org/10.15388/Informatica.2011.315
Pub. online: 1 January 2011      Type: Research Article     

Received
1 October 2009
Accepted
1 December 2010
Published
1 January 2011

Abstract

To set the values of the hyperparameters of a support vector machine (SVM), the method of choice is cross-validation. Several upper bounds on the leave-one-out error of the pattern recognition SVM have been derived. One of the most popular is the radius–margin bound. It applies to the hard margin machine, and, by extension, to the 2-norm SVM. In this article, we introduce the first quadratic loss multi-class SVM: the M-SVM2. It can be seen as a direct extension of the 2-norm SVM to the multi-class case, which we establish by deriving the corresponding generalized radius–margin bound.

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Keywords
multi-class SVMs model selection leave-one-out cross-validation error radius–margin bounds

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INFORMATICA

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