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dc.contributor.authorGámez Martín, José Antonio
dc.contributor.authorRumí, Rafael
dc.contributor.authorSalmerón Cerdán, Antonio
dc.date.accessioned2012-05-28T09:50:18Z
dc.date.available2012-05-28T09:50:18Z
dc.date.issued2006
dc.identifier.citationProceedings of the Third European Workshop on Probabilistic Graphical Models (PGM'06), pp. 123-132.es_ES
dc.identifier.urihttp://hdl.handle.net/10835/1555
dc.description.abstractIn this paper we propose a naive Bayes model for unsupervised data clustering, where the class variable is hidden. The feature variables can be discrete or continuous, as the conditional distributions are represented as mixtures of truncated exponentials (MTEs). The number of classes is determined using the data augmentation algorithm. The proposed model is compared with the conditional Gaussian model for some real world and synthetic databases.es_ES
dc.language.isoenes_ES
dc.sourceThird European Workshop on Probabilistic Graphical Models (PGM'06)es_ES
dc.titleUnsupervised naive Bayes for data clustering 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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