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                <term xml:lang="fr">classifieur hybride</term>
                <term xml:lang="fr">arbre de décision flou</term>
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              <p>En reconnaissance des formes, il estsouvent difficile de concevoir des systèmes à la fois performants, génériqueset interprétables. Pourtant, l'interprétabilité dusystème permet à un expert de le maintenir et d'optimiser sesperformances (taux de reconnaissance, fiabilité, occupation mémoire, ...). Afin de répondre à cesobjectifs, nous proposons un nouveau classifieur hybride qui reposesur une combinaison originale de deux niveauxde modélisation complémentaires basés sur des connaissancesintrinsèques et discriminantes. Les informations sont modélisées defaçon robuste et explicite par des sous-ensembles flous extraits de manièreautomatique. Des systèmes d'inférence floue permettent alors d'agrégeret de fusionner les connaissances afin d'obtenir un processus de décision robuste et compréhensible. Lesexpérimentations reportées dans cet article valident aussiles propriétés de généricité et de performances.</p>
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