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INFORMATICALT
2011

A Quadratic Loss Multi-Class SVM for which a Radius-Margin Bound Applies

9 years 6 months ago
A Quadratic Loss Multi-Class SVM for which a Radius-Margin Bound Applies
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.
Yann Guermeur, Emmanuel Monfrini
Added 14 May 2011
Updated 14 May 2011
Type Journal
Year 2011
Where INFORMATICALT
Authors Yann Guermeur, Emmanuel Monfrini
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