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Bounds on the Risk for M-SVMs

Yann Guermeur 1 André Elisseeff 2 Dominique Zelus
1 MODBIO - Computational models in molecular biology
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : Vapnik's statistical learning theory has mainly been developed for two types of problems: pattern recognition (computation of dichotomies) and regression (estimation of real-valued functions). Only in recent years has multi-class discriminant analysis been studied independently. Extending several standard results, among which a famous theorem by Bartlett, we have derived distribution-free uniform strong laws of large numbers devoted to multi-class large margin discriminant models. The capacity measure appearing in the confidence interval, a covering number, has been bounded from above in terms of a new generalized VC dimension. In this paper, the aforementioned theorems are applied to the architecture shared by all the multi-class SVMs proposed so far, which provides us with a simple theoretical framework to study them, compare their performance and design new machines.
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Submitted on : Tuesday, September 26, 2006 - 9:39:00 AM
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  • HAL Id : inria-00099587, version 1



Yann Guermeur, André Elisseeff, Dominique Zelus. Bounds on the Risk for M-SVMs. Applied Stochastic Models in Business and Industry, Wiley, 2003. ⟨inria-00099587⟩



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