De l'identification de structure de réseaux bayésiens à la reconnaissance de formes à partir d'informations complètes ou incomplètes. (Bayesian Network Identification from Complete or Incomplete Datasets)

Durant ces travaux de these, une comparaison empirique de differentes techniques d'apprentissage de structure de reseaux bayesiens a ete effectuee, car meme s'il peut en exister tres ponctuellement, il n'existe pas de comparaisons plus globales de ces algorithmes. De multiples phases de tests nous ont permis d'identifier quelles methodes souffraient de difficultes d'initialisation et nous avons propose une technique pour les resoudre. Nous avons ensuite adapte differentes methodes d'apprentissage de structure aux bases de donnees incompletes et avons notamment introduit une technique pour apprendre efficacement une structure arborescente. Cette methode est ensuite adaptee a la problematique plus specifique de la classification et permet d'apprendre efficacement et en toute generalite un classifieur de Bayes Naif augmente. Un formalisme original permettant de generer des bases de donnees incompletes ayant des donnees manquantes verifiant les hypotheses MCAR ou MAR est egalement introduit. De nombreuses bases synthetiques ou reelles ont alors ete utilisees pour tester ces methodes d'apprentissage de structure a partir de bases incompletes.

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