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dc.contributor.author Daly, Ronan
dc.contributor.author Aitken, Stuart
dc.contributor.author Shen, Qiang
dc.date.accessioned 2008-01-15T21:21:26Z
dc.date.available 2008-01-15T21:21:26Z
dc.date.issued 2006
dc.identifier.citation Daly , R , Aitken , S & Shen , Q 2006 , ' Speeding up the learning of equivalence classes of Bayesian network structures ' pp. 34-39 . en
dc.identifier.other PURE: 74407
dc.identifier.other dspace: 2160/439
dc.identifier.uri http://hdl.handle.net/2160/439
dc.description R. Daly, Q. Shen and S. Aitken. Speeding up the learning of equivalence classes of Bayesian network structures. Proceedings of the 10th International Conference on Artificial Intelligence and Soft Computing, pages 34-39. en
dc.description.abstract For some time, learning Bayesian networks has been both feasible and useful in many problems domains. Recently research has been done on learning equivalence classes of Bayesian networks, i.e. structures that capture all of the graphical information of a group of Bayesian networks, in order to increase learning speed and quality. However learning speed still remains quite slow, especially on problems with many variables. This work aims to describe a method to speed up algorithm learning speed. A brief overview of learning Bayesian networks is given. A method is then given, so that tests of whether a particular move is valid can be cached. Finally, experiments are conducted, which show that applying this caching method produces a marked increase in learning speed. en
dc.format.extent 6 en
dc.language.iso eng
dc.relation.ispartof en
dc.title Speeding up the learning of equivalence classes of Bayesian network structures en
dc.type Text en
dc.type.publicationtype Conference paper en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Advanced Reasoning Group en
dc.description.status Non peer reviewed en


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