A Quadratic Loss Multi-Class SVM

Emmanuel Monfrini (1) , Yann Guermeur (1)

1 ABC - Machine Learning and Computational Biology

Using a support vector machine requires to set two types of hyperparameters: the soft margin parameter C and the parameters of the kernel. To perform this model selection task, the method of choice is cross-validation. Its leave-one-out variant is known to produce an estimator of the generalization error which is almost unbiased. Its major drawback rests in its time requirement. To overcome this difficulty, several upper bounds on the leave-one-out error of the pattern recognition SVM have been derived. Among those bounds, the most popular one is probably the radius-margin bound. It applies to the hard margin pattern recognition SVM, and by extension to the 2-norm SVM. In this report, we introduce a quadratic loss M-SVM, the M-SVM^2, as a direct extension of the 2-norm SVM to the multi-class case. For this machine, a generalized radius-margin bound is then established.

Mots clés: M-SVMs; model selection; leave-one-out error; radius-margin bound.; radius-margin bound

Domaines: Apprentissage [cs.LG]

Emmanuel Monfrini, Yann Guermeur. A Quadratic Loss Multi-Class SVM. 2008. ⟨hal-00276700⟩