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dc.contributor.authorRomero, Vanessa
dc.contributor.authorRumí, Rafael
dc.contributor.authorSalmerón Cerdán, Antonio
dc.date.accessioned2012-05-28T09:50:29Z
dc.date.available2012-05-28T09:50:29Z
dc.date.issued2004
dc.identifier.citationProceedings of the Second European Workshop on Probabilistic Graphical Models (PGM'04), pp. 177-184.es_ES
dc.identifier.urihttp://hdl.handle.net/10835/1556
dc.description.abstractIn this paper we introduce a hill-climbing algorithm for structural learning of Bayesian networks from databases with discrete and continuous variables. The process is based on the optimisation of a metric that measures the accuracy of a network penalised by its complexity. The result of the algorithm is a network where the conditional distribution for each variable is a mixture of truncated exponentials (MTE), so that no restrictions on the network topology are imposed. The using artificial and real world data.es_ES
dc.language.isoenes_ES
dc.sourceSecond European Workshop on Probabilistic Graphical Models (PGM'04)es_ES
dc.titleStructural Learning of Bayesian Networks with Mixtures of Truncated Exponentialses_ES
dc.typeinfo:eu-repo/semantics/reportes_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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