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Learning dynamic Bayesian network models via cross-validation
Linköping University, Department of Computer and Information Science, Database and information techniques. (ADIT)
Björkegren, J., Center for Genomics and Bioinformatics, Karolinska Institute, 17177 Stockholm, Sweden.
Linköping University, The Institute of Technology. Linköping University, Department of Physics, Chemistry and Biology, Computational Biology.
2005 (English)In: Pattern Recognition Letters, ISSN 0167-8655, Vol. 26, no 14, 2295-2308 p.Article in journal (Refereed) Published
Abstract [en]

We study cross-validation as a scoring criterion for learning dynamic Bayesian network models that generalize well. We argue that cross-validation is more suitable than the Bayesian scoring criterion for one of the most common interpretations of generalization. We confirm this by carrying out an experimental comparison of cross-validation and the Bayesian scoring criterion, as implemented by the Bayesian Dirichlet metric and the Bayesian information criterion. The results show that cross-validation leads to models that generalize better for a wide range of sample sizes. © 2005 Elsevier B.V. All rights reserved.

Place, publisher, year, edition, pages
2005. Vol. 26, no 14, 2295-2308 p.
Keyword [en]
Cross-validation, Dynamic Bayesian network models, Learning.
National Category
Engineering and Technology
URN: urn:nbn:se:liu:diva-50403DOI: 10.1016/j.patrec.2005.04.005OAI: diva2:271299
Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2013-05-14

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Peña, Jose M.Tegnér, Jesper
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