(Semi-)Predictive Discretization During Model Selection

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/6709

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DC Field	Value	Language
dc.creator	Steck, Harald	-
dc.creator	Jaakkola, Tommi S.	-
dc.date	2004-10-08T20:38:42Z	-
dc.date	2004-10-08T20:38:42Z	-
dc.date	2003-02-25	-
dc.date.accessioned	2013-10-09T02:46:31Z	-
dc.date.available	2013-10-09T02:46:31Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AIM-2003-002	-
dc.identifier	http://hdl.handle.net/1721.1/6709	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	In this paper, we present an approach to discretizing multivariate continuous data while learning the structure of a graphical model. We derive the joint scoring function from the principle of predictive accuracy, which inherently ensures the optimal trade-off between goodness of fit and model complexity (including the number of discretization levels). Using the so-called finest grid implied by the data, our scoring function depends only on the number of data points in the various discretization levels. Not only can it be computed efficiently, but it is also independent of the metric used in the continuous space. Our experiments with gene expression data show that discretization plays a crucial role regarding the resulting network structure.	-
dc.format	15 p.	-
dc.format	4299414 bytes	-
dc.format	910469 bytes	-
dc.format	application/postscript	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AIM-2003-002	-
dc.subject	AI	-
dc.subject	Discretization	-
dc.subject	Graphical models	-
dc.title	(Semi-)Predictive Discretization During Model Selection	-
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