On approximating K-Most probable explanations of Bayesian networks using genetic algorithms

dc.contributor.advisorSurapong Auwatanamongkol, advisorth
dc.contributor.authorNalerk Sriwachirawatth
dc.date.accessioned2014-05-05T08:54:58Z
dc.date.available2014-05-05T08:54:58Z
dc.date.issued2006th
dc.date.issuedBE2549th
dc.descriptionThesis (M.S. (Computer Science))--National Institute of Development Administration, 2006.th
dc.format.extentxiii, 117 leaves ; 30 cm.th
dc.format.mimetypeapplication/pdfth
dc.identifier.urihttp://repository.nida.ac.th/handle/662723737/422th
dc.language.isoength
dc.publisherNational Institute of Development Administrationth
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.th
dc.subject.lccQA 76.623 N147 2006th
dc.subject.otherGenetic algorithmsth
dc.subject.otherGenetic programming (Computer science)th
dc.subject.otherArtificial intelligenceth
dc.titleOn approximating K-Most probable explanations of Bayesian networks using genetic algorithmsth
dc.typetext--thesis--master thesisth
mods.genreThesisth
mods.physicalLocationNational Institute of Development Administration. Library and Information Centerth
thesis.degree.departmentSchool of Applied Statisticsth
thesis.degree.disciplineComputer Scienceth
thesis.degree.grantorNational Institute of Development Administrationth
thesis.degree.levelMastersth
thesis.degree.nameMaster of Scienceth
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
nida-ths-b150085.pdf
Size:
16.8 MB
Format:
Adobe Portable Document Format
Description:
Full Text
Collections