Najeh Naffakhi

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In this paper, we are interested in aggregated search in structured XML documents. We present a model for the structured information retrieval, based on the Bayesian networks theory. Relations query-terms and terms-elements are modelled through probability measures. In this model, the user's query starts a process of propagation to recover the relevant and(More)
In this paper, we are interested in aggregated search in structured XML documents. We present a structured information retrieval model based on the Bayesian networks theory. Query-terms and terms-elements relations are modeled through probability. In this model, the user's query starts a propagation process to recover the XML elements. Thus, instead of(More)
In this paper, we are interested in aggregated search in XML documents. Our goal is to retrieve the best set of XML elements to be returned. We present a structured information retrieval model based on the Bayesian networks theory. The networks structure provides a natural representation of links between a document, its elements, and its contents. In this(More)
In this paper, we are interested in aggregated search in structured XML documents. We present a structured information retrieval model based on the Bayesian networks theory. Relations query-terms and terms-elements are modeled through probability. In this model, the user's query starts a process of propagation to recover the elements. Thus, instead of(More)
Un problème important de la production automatique de règles de classification concerne la durée de génération de ces règles ; en effet, les algorithmes mis en oeuvre produisent souvent des règles pendant un certain temps assez long. Nous proposons une nouvelle méthode de classification à partir d'une base de données images. Cette méthode se situe à la(More)
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