HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

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.
Document type :
Conference papers
Complete list of metadata

Contributor : Frédéric Sur Connect in order to contact the contributor
Submitted on : Friday, October 6, 2006 - 11:34:40 AM
Last modification on : Friday, February 4, 2022 - 3:31:00 AM


  • HAL Id : inria-00104299, version 1



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. ⟨inria-00104299⟩



Record views