Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7648
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dc.contributor.authorWong, Man Leungen_US
dc.contributor.authorLee, Shing Yanen_US
dc.contributor.authorProf. LEUNG Kwong Saken_US
dc.date.accessioned2023-03-29T04:56:07Z-
dc.date.available2023-03-29T04:56:07Z-
dc.date.issued2002-
dc.identifier.citationProceedings of the 2002 Congress on Evolutionary Computation, CEC 2002, 2002, vol. 2, pp. 1314 - 1319, Article number 1004433en_US
dc.identifier.isbn0780372824-
dc.identifier.isbn978-078037282-5-
dc.identifier.urihttp://hdl.handle.net/20.500.11861/7648-
dc.description.abstractA novel hybrid framework is reported that improves upon our previous work, MDLEP, which uses evolutionary programming to solve the difficult Bayesian network learning problem. A new merge operator is also introduced that further enhances the efficiency. As experimental results suggest, our hybrid approach performs significantly better than MDLEP. © 2002 IEEE.en_US
dc.language.isoenen_US
dc.publisherIEEE Computer Societyen_US
dc.relation.ispartofProceedings of the 2002 Congress on Evolutionary Computation, CEC 2002en_US
dc.titleA hybrid approach to learn Bayesian networks using evolutionary programmingen_US
dc.typeConference Paperen_US
dc.identifier.doi10.1109/CEC.2002.1004433-
item.fulltextNo Fulltext-
crisitem.author.deptDepartment of Applied Data Science-
Appears in Collections:Applied Data Science - Publication
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