Bounding the Capacity Measure of Multi-Class Discriminant Models

Yann Guermeur 1 André Elisseeff 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). Multi-class discriminant analysis has only been studied independently in recent years. 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 discriminant models. This technical report deals with the computation of the capacity measures involved in these bounds on the expected risk. It considers more specifically the case of multi-class SVMs.
Type de document :
[Intern report] A02-R-028 || guermeur02b, 2002, 20 p
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Contributeur : Publications Loria <>
Soumis le : mardi 26 septembre 2006 - 14:50:07
Dernière modification le : jeudi 11 janvier 2018 - 06:19:51


  • HAL Id : inria-00100733, version 1



Yann Guermeur, André Elisseeff, Dominique Zelus. Bounding the Capacity Measure of Multi-Class Discriminant Models. [Intern report] A02-R-028 || guermeur02b, 2002, 20 p. 〈inria-00100733〉



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