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1 QGAR - Querying Graphics through Analysis and Recognition INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications

Abstract : We present in this paper an original adaptation of Bayesian networks to symbol recognition problem. More precisely, a descriptor combination method, which enables to improve significantly the recognition rate compared to the recognition rates obtained by each descriptor, is presented. In this perspective, we use a simple Bayesian classifier, called naive Bayes. In fact, probabilistic graphical models, more specifically Bayesian networks, are a simple and intuitive way of probability distribution representation. In order to solve the dimensionality problem, we use a variable selection method. Experimental results, obtained in a supervised learning context and tested on GREC symbol database, are very promising.

Keywords : Symbol recognition data analysis probabilistic graphical models Bayesian networks variable selection





Autor: Sabine Barrat - Salvatore Tabbone - Patrick Nourrissier -

Fuente: https://hal.archives-ouvertes.fr/



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