Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7668
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dc.contributor.authorWong, Man Leungen_US
dc.contributor.authorLam, Waien_US
dc.contributor.authorProf. LEUNG Kwong Saken_US
dc.date.accessioned2023-03-30T03:12:50Z-
dc.date.available2023-03-30T03:12:50Z-
dc.date.issued1999-
dc.identifier.citationIEEE Transactions on Pattern Analysis and Machine Intelligence, 1999, Vol. 21 (2), pp. 174 - 178en_US
dc.identifier.issn01628828-
dc.identifier.urihttp://hdl.handle.net/20.500.11861/7668-
dc.description.abstractWe have developed a new approach (MDLEP) to learning Bayesian network structures based on the Minimum Description Length (MDL) principle and Evolutionary Programming (EP). It employs a MDL metric which is founded on information theory and integrates a knowledge-guided genetic operator for the optimization in the search process. ©1999 IEEE.en_US
dc.language.isoenen_US
dc.relation.ispartofIEEE Transactions on Pattern Analysis and Machine Intelligenceen_US
dc.titleUsing evolutionary programming and minimum description length principle for data mining of bayesian networksen_US
dc.typePeer Reviewed Journal Articleen_US
dc.identifier.doi10.1109/34.748825-
item.fulltextNo Fulltext-
crisitem.author.deptDepartment of Applied Data Science-
Appears in Collections:Applied Data Science - Publication
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