Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/10261/3019
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dc.creatorCerquides, Jesus-
dc.creatorLopez de Mantaras, Ramon-
dc.date2008-02-21T13:05:36Z-
dc.date2008-02-21T13:05:36Z-
dc.date2005-
dc.date.accessioned2017-01-31T01:00:18Z-
dc.date.available2017-01-31T01:00:18Z-
dc.identifierMachine Learning, 2005, 59 (3): 323-354-
dc.identifier0885-6125-
dc.identifierhttp://hdl.handle.net/10261/3019-
dc.identifier.urihttp://dspace.mediu.edu.my:8181/xmlui/handle/10261/3019-
dc.descriptionThe original publication is available at www.springerlink.com-
dc.descriptionIn this paper we present several Bayesian algorithms for learning Tree Augmented Naive Bayes (TAN) models. We extend the results in Meila & Jaakkola (2000a) to TANs by proving that accepting a prior decomposable distribution over TAN's, we can compute the exact Bayesian model averaging over TAN structures and parameters in polynomial time. Furthermore, we prove that the k-maximum a posteriori (MAP) TAN structures can also be computed in polynomial time. We use these results to correct minor errors in Meila & Jaakkola (2000a) and to construct several TAN based classifiers provide consistently better predictions over Irvine datasets and artificially generated data than TAN based classifiers proposed in the literature.-
dc.descriptionPeer reviewed-
dc.format426079 bytes-
dc.formatapplication/pdf-
dc.languageeng-
dc.publisherSpringer-
dc.rightsopenAccess-
dc.subjectArtificial Intelligence-
dc.subjectBayesian networks classifiers-
dc.subjectNaive Bayes-
dc.subjectTree augmented naive Bayes-
dc.subjectDecomposable distributions-
dc.subjectBayesian model averaging-
dc.titleTAN Classifiers Based on Decomposable Distributions-
dc.typeArtículo-
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