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dc.contributor.authorSteck, Haralden_US
dc.contributor.authorJaakkola, Tommi S.en_US
dc.date.accessioned2004-10-08T20:38:42Z
dc.date.available2004-10-08T20:38:42Z
dc.date.issued2003-02-25en_US
dc.identifier.otherAIM-2003-002en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/6709
dc.description.abstractIn 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.en_US
dc.format.extent15 p.en_US
dc.format.extent4299414 bytes
dc.format.extent910469 bytes
dc.format.mimetypeapplication/postscript
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.relation.ispartofseriesAIM-2003-002en_US
dc.subjectAIen_US
dc.subjectDiscretizationen_US
dc.subjectGraphical modelsen_US
dc.title(Semi-)Predictive Discretization During Model Selectionen_US


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