Estimating Dependency Structure as a Hidden Variable

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/7257

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DC Field	Value	Language
dc.creator	Meila, Marina	-
dc.creator	Jordan, Michael I.	-
dc.creator	Morris, Quaid	-
dc.date	2004-10-20T21:04:25Z	-
dc.date	2004-10-20T21:04:25Z	-
dc.date	1998-09-01	-
dc.date.accessioned	2013-10-09T02:48:49Z	-
dc.date.available	2013-10-09T02:48:49Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AIM-1648	-
dc.identifier	CBCL-165	-
dc.identifier	http://hdl.handle.net/1721.1/7257	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	This paper introduces a probability model, the mixture of trees that can account for sparse, dynamically changing dependence relationships. We present a family of efficient algorithms that use EM and the Minimum Spanning Tree algorithm to find the ML and MAP mixture of trees for a variety of priors, including the Dirichlet and the MDL priors. We also show that the single tree classifier acts like an implicit feature selector, thus making the classification performance insensitive to irrelevant attributes. Experimental results demonstrate the excellent performance of the new model both in density estimation and in classification.	-
dc.format	1320254 bytes	-
dc.format	477415 bytes	-
dc.format	application/postscript	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AIM-1648	-
dc.relation	CBCL-165	-
dc.title	Estimating Dependency Structure as a Hidden Variable	-
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