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Sélection de variables pour l'apprentissage simultanée de tâches

Abstract : Recently, there has been a lot of interest around multi-task learning (MTL) problem with the constraints that tasks should share common features. Such a problem can be addressed through a regularization framework where the regularizer induces a joint-sparsity pattern between task decision functions. We follow this principled framework but instead we focus on lp − l2 (with p ≤ 1) mixed-norms as sparsity-inducing penalties. After having shown that the l1 − l2 MTL problem is a general case of Multiple Kernel Learning (MKL), we adapted the available efficient tools of solving MKL to the sparse MTL problem. Then, for the more general case when p < 1, the use of a DC program provides an iterative scheme solving at each iteration a weighted 1 − 2 sparse MTL problem.
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Submitted on : Tuesday, February 2, 2010 - 11:03:54 AM
Last modification on : Wednesday, March 2, 2022 - 10:10:08 AM
Long-term archiving on: : Thursday, October 18, 2012 - 2:06:25 PM


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  • HAL Id : hal-00452332, version 1


Rémi Flamary, Alain Rakotomamonjy, Gilles Gasso, Stephane Canu. Sélection de variables pour l'apprentissage simultanée de tâches. Confrénce D'Apprentissage (CAp), May 2009, Hammamet, France. pp.109-120. ⟨hal-00452332⟩



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