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Tag - multiple kernel

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lundi, septembre 14 2009

Using Kernel Basis with Relevance Vector Machine for Feature Selection

19th International Conference on Artificial Neural Networks 14-17 September, Limassol, Cyprus http://www.kios.org.cy/ICANN09/

Abstract This paper presents an application of multiple kernels like Kernel Basis to the Relevance Vector Machine algorithm. The framework of kernel machines has been a source of many works concerning the merge of various kernels to build the solution. Within these approaches, Kernel Basis is able to combine both local and global kernels. The interest of such approach resides in the ability to deal with a large kind of tasks in the field of model selection, for example the feature selection. We propose here an application of RVM-KB to a feature selection problem, for which all data are decomposed into a set of kernels so that all points of the learning set correspond to a single feature of one data. The final result is the selection of the main features through the relevance vectors selection.

@inproceedings{fsuardicann09,

author = {Frédéric Suard and David Mercier},
title = {Using Kernel Basis with Relevance Vector Machine for Feature Selection},
booktitle = {ICANN 2009, Part II},
editor = {LNCS},
publisher = {Springer-Verlag},
year = {2009},
pages = {255-264},

}

mardi, septembre 8 2009

Application des noyaux multiples de type Kernel Basis à la méthode Relevance Vector Machine pour la sélection de modèles

20e colloque GRETSI sur le traitement du signal et des images 8-11 septembre 2009, Dijon, France. http://www.gretsi2009.org

Abstract This paper presents an extension of multiple kernels like Kernel Basis to the Relevance Vector Machine algorithm. The framework of kernel machines has been a source of many works concerning the merge of various kernels to build the solution. Within these approaches, Kernel Basis is able to combine both local and global kernels. The interest of such approach resides in the ability to deal with a large kind of tasks in the field of model selection, for example the feature selection. We propose here an application of RVM-KB to a feature selection problem, for which all data are decomposed into a set of kernels so that all points of the learning set correspond to a single feature of one data. The final result is the selection of the main features through the relevance vectors selection.

Résumé – Nous présentons ici une adaptation des noyaux multiples de type Kernel Basis à l'algorithme Relevance Vector Machine. L'intérêt du Kernel Basis réside dans la capacité d'adapter des noyaux globaux et locaux dans une même solution. La finalité consiste en effet à affecter aux vecteurs de la solution un ensemble de noyaux qui soit spécifique à chaque vecteur. Nous proposons d'utiliser cette approche pour un problème de sélection de variables, afin de choisir pour chaque vecteur les variables les plus pertinentes. Les performances obtenues sur les résultats préliminaires et comparées avec une approche de noyau multiple composite, sont très prometteuses et ouvrent de nouvelles perspectives.

@inproceedings{fsuardgretsi09,
author = {Frédéric Suard and David Mercier},
title = {Application des noyaux multiples de type Kernel Basis à la méthode Relevance Vector Machine pour la sélection de modèles},
booktitle = {GRETSI09, Dijon, France},
year = {2009},
pages = {4p},}

mardi, septembre 11 2007

Noyaux multiples : sélection de modèle appliquée à la détection de piétons

21e colloque GRETSI sur le traitement du signal et des images 11-14 septembre 2007, Troyes, France www.gretsi2007.org

Abstract This paper presents a pedestrian detection method based on the multiple kernel framework. One main problematic of pattern recognition resides in the pertinent characterization of the data. Depending on the descriptor, we sometimes have to tune the descriptor in order to be more efficient. Instead of accomplishing this tuning manually by testing and comparing all possible values we propose here to use the multiple kernel framework. The aim is to use a kernel as a linear combination of different kernels in order to combine and select automatically the best kernels within a set of kernels. This can be assimilated as model selection, where one kernel of the set corresponds to one model. We first introduce the MKL framework and finally apply this approach for a parameter tuning task and a feature selection problem.