Model selection for multi-class SVMs

Yann Guermeur 1 Myriam Maumy 2 Frédéric Sur 1
1 MODBIO - Computational models in molecular biology
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : In the framework of statistical learning, fitting a model to a given problem is usually done in two steps. First, model selection is performed, to set the values of the hyperparameters. Second, training results in the selection, for this set of values, of a function performing satisfactorily on the problem. Choosing the values of the hyperparameters remains a difficult task, which has only been addressed so far in the case of bi-class SVMs. We derive here a solution dedicated to M-SVMs. It is based on a new bound on the risk of large margin classifiers.
Type de document :
Communication dans un congrès
International Symposium on Applied Stochastic Models and Data Analysis - ASMDA 2005, May 2005, Brest, France. 2005
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https://hal.inria.fr/inria-00104299
Contributeur : Frédéric Sur <>
Soumis le : vendredi 6 octobre 2006 - 11:34:40
Dernière modification le : jeudi 11 janvier 2018 - 06:19:51

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  • HAL Id : inria-00104299, version 1

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Yann Guermeur, Myriam Maumy, Frédéric Sur. Model selection for multi-class SVMs. International Symposium on Applied Stochastic Models and Data Analysis - ASMDA 2005, May 2005, Brest, France. 2005. 〈inria-00104299〉

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