Alain Rakotomamonjy alain rouen fr LITIS EA Universite de Rouen Saint Etienne du Rouvray France Francis Bach francis org INRIA Willow project Departement d'Informatique Ecole Normale Superieure Rue d'Ulm Paris France Stephane Canu stephane rouen fr LITIS EA INSA de Rouen Saint Etienne du Rouvray France

Alain Rakotomamonjy

Description

Niveau: Supérieures
SimpleMKL Alain Rakotomamonjy LITIS EA 4108 Universite de Rouen 76800 Saint Etienne du Rouvray, France Francis Bach INRIA - Willow project Departement d'Informatique, Ecole Normale Superieure 45, Rue d'Ulm 75230 Paris, France Stephane Canu LITIS EA 4108 INSA de Rouen 76801 Saint Etienne du Rouvray, France Yves Grandvalet CNRS/IDIAP Research Institute, Centre du Parc, Av. des Pres-Beudin 20 1920 Martigny, Switzerland Abstract Multiple kernel learning aims at simultaneously learning a kernel and the associated pre- dictor in supervised learning settings. For the support vector machine, an efficient and general multiple kernel learning (MKL) algorithm, based on semi-infinite linear progam- ming, has been recently proposed. This approach has opened new perspectives since it makes the MKL approach tractable for large-scale problems, by iteratively using exist- ing support vector machine code. However, it turns out that this iterative algorithm needs numerous iterations for converging towards a reasonable solution. In this pa- per, we address the MKL problem through an adaptive 2-norm regularization formu- lation that encourages sparse kernel combinations. Apart from learning the combina- tion, we solve a standard SVM optimization problem, where the kernel is defined as a linear combination of multiple kernels.

• vector machine


• algorithm


• svm


• svm problems


• kernel learning


• norm regularization


• mkl problem


• like regression

Sujets

• Algorithm

Informations

• Publié par: profil-fouv-2012
• Nombre de lectures: 136
• Langue: English

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