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dc.contributor.authorCocu, Adina
dc.date.accessioned2016-01-21T09:54:48Z
dc.date.available2016-01-21T09:54:48Z
dc.date.issued2008
dc.identifier.urihttp://10.11.10.50/xmlui/handle/123456789/3917
dc.descriptionThe Annals of "Dunarea de Jos" University of Galatien_US
dc.description.abstractLearning a Bayesian network from a numeric set of data is a challenging task because of dual nature of learning process: initial need to learn network structure, and then to find out the distribution probability tables. In this paper, we propose a machine-learning algorithm based on hill climbing search combined with Tabu list. The aim of learning process is to discover the best network that represents dependences between nodes. Another issue in machine learning procedure is handling numeric attributes. In order to do that, we must perform an attribute discretization pre-processes. This discretization operation can influence the results of learning network structure. Therefore, we make a comparative study to find out the most suitable combination between discretization method and learning algorithm, for a specific data set.en_US
dc.language.isoenen_US
dc.publisher"Dunarea de Jos" University of Galatien_US
dc.subjectBayes networken_US
dc.subjectstructure learningen_US
dc.subjectstudent modellingen_US
dc.subjectintelligent tutoringen_US
dc.titleLearning Bayesian Dependence Model for Student Modellingen_US
dc.typeArticleen_US


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