Linear kernel combination using boosting

Alexis Lechervy 1 Philippe-Henri Gosselin 2 Frédéric Precioso 3
2 MIDI
ETIS - Equipes Traitement de l'Information et Systèmes
3 Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Equipe KEIA
SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
Abstract : In this paper, we propose a novel algorithm to design multi- class kernels based on an iterative combination of weak kernels in a schema inspired from the boosting framework. Our solution has a complexity lin- ear with the training set size. We evaluate our method for classification on a toy example by integrating our multi-class kernel into a kNN clas- sifier and comparing our results with a reference iterative kernel design method. We also evaluate our method for image categorization by con- sidering a classic image database and comparing our boosted linear kernel combination with the direct linear combination of all features in a linear SVM.
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Communication dans un congrès
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Apr 2012, Bruges, Belgium. pp.6, 2012
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https://hal.archives-ouvertes.fr/hal-00753155
Contributeur : Philippe-Henri Gosselin <>
Soumis le : samedi 17 novembre 2012 - 18:47:19
Dernière modification le : mardi 28 octobre 2014 - 18:41:56
Document(s) archivé(s) le : lundi 18 février 2013 - 03:42:37

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Alexis Lechervy, Philippe-Henri Gosselin, Frédéric Precioso. Linear kernel combination using boosting. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Apr 2012, Bruges, Belgium. pp.6, 2012. <hal-00753155>

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