Estimating Dependency Structure as a Hidden Variable

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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