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dc.contributor.author Shen, Qiang
dc.contributor.author Daly, Ronan
dc.date.accessioned 2008-01-15T14:56:12Z
dc.date.available 2008-01-15T14:56:12Z
dc.date.issued 2007
dc.identifier.citation Shen , Q & Daly , R 2007 , ' Methods to accelerate the learning of bayesian network structures ' . en
dc.identifier.other PURE: 74244
dc.identifier.other dspace: 2160/421
dc.identifier.uri http://hdl.handle.net/2160/421
dc.description R. Daly and Q. Shen. Methods to accelerate the learning of bayesian network structures. Proceedings of the Proceedings of the 2007 UK Workshop on Computational Intelligence. en
dc.description.abstract Bayesian networks have become a standard technique in the representation of uncertain knowledge. This paper proposes methods that can accelerate the learning of a Bayesian network structure from a data set. These methods are applicable when learning an equivalence class of Bayesian network structures whilst using a score and search strategy. They work by constraining the number of validity tests that need to be done and by caching the results of validity tests. The results of experiments show that the methods improve the performance of algorithms that search through the space of equivalence classes multiple times and that operate on wide data sets. The experiments were performed by sampling data from six standard Bayesian networks and running an ant colony optimization algorithm designed to learn a Bayesian network equivalence class. en
dc.language.iso eng
dc.title Methods to accelerate the learning 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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