Maximum entropy Gibbs density modeling for pattern classification [r-libre/392]

Mezghani, Neila; Mitiche, Amar et Cheriet, Mohamed (2012). Maximum entropy Gibbs density modeling for pattern classification. Entropy, 14, 2478-2491. https://doi.org/10.3390/e14122478

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Catégorie de document :	Articles de revues
Évaluation par un comité de lecture :	Oui
Étape de publication :	Publié
Résumé :	Recent studies have shown that the Gibbs density function is a good model for visual patterns and that its parameters can be learned from pattern category training data by a gradient algorithm optimizing a constrained entropy criterion. These studies represented each pattern category by a single density. However, the patterns in a category can be so complex as to require a representation spread over several densities to more accurately account for the shape of their distribution in the feature space. The purpose of the present study is to investigate a representation of visual pattern category by several Gibbs densities using a Kohonen neural structure. In this Gibbs density based Kohonen network, which we call a Gibbsian Kohonen network, each node stores the parameters of a Gibbs density. Collectively, these Gibbs densities represent the pattern category. The parameters are learned by a gradient update rule so that the corresponding Gibbs densities maximize entropy subject to reproducing observed feature statistics of the training patterns. We verified the validity of the method and the efficiency of the ensuing Gibbs density pattern representation on a handwritten character recognition application.
Déposant:	Mezghani, Neila
Responsable :	Neila Mezghani
Dépôt :	14 oct. 2014 14:04
Dernière modification :	16 juill. 2015 00:46

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