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Séparateurs à Vaste Marge pondérés en norme l2 pour la sélection de variables en apprentissage d’ordonnancement

Abstract : Learning to rank algorithms are dealing with a very large amount of features to automatically learn ranking functions, which leads to an increase of both the computational cost and the number of noisy redundant features. Feature selection is seen as a promising way to address these issues. In this paper, we propose new feature selection algorithms for learning to rank based on reweighted l2 SVM approaches. We investigate a l2-AROM algorithm to solve the l0 norm optimization problem and a generic l2-reweighted algorithm to approximate l0 et l1 norm SVM problems with l2 norm SVM. Experiments show that our algorithms are up to 10 times faster and use up to 7 times less features than state-of-the-art methods, without lowering the ranking performance.
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https://hal.inria.fr/hal-01259553
Contributor : Léa Laporte <>
Submitted on : Wednesday, January 20, 2016 - 3:54:26 PM
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Léa Laporte, Sébastien Dejean, Josiane Mothe. Séparateurs à Vaste Marge pondérés en norme l2 pour la sélection de variables en apprentissage d’ordonnancement. Conférence francophone en Recherche d'Information et Applications (CORIA 2014), Mar 2014, Nancy, France. pp.16. ⟨hal-01259553⟩

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