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dc.contributor.authorCorander, Jukka
dc.contributor.authorJanhunen, Tomi
dc.contributor.authorRintanen, Jussi
dc.contributor.authorNyman, Henrik
dc.contributor.authorPensar, Johan
dc.date.accessioned2022-11-23T00:17:28Z
dc.date.available2022-11-23T00:17:28Z
dc.date.issued2013
dc.date.modified2014-07-07T22:14:17Z
dc.identifier.isbn9781632660244en_US
dc.identifier.urihttp://hdl.handle.net/10072/61104
dc.description.abstractWe investigate the problem of learning the structure of a Markov network from data. It is shown that the structure of such networks can be described in terms of constraints which enables the use of existing solver technology with optimization capabilities to compute optimal networks starting from initial scores computed from the data. To achieve efficient encodings, we develop a novel characterization of Markov network structure using a balancing condition on the separators between cliques forming the network. The resulting translations into propositional satisfiability and its extensions such as maximum satisfiability, satisfiability modulo theories, and answer set programming, enable us to prove optimal certain network structures which have been previously found by stochastic search.en_US
dc.description.publicationstatusYes
dc.languageEnglishen_US
dc.publisherNeural Information Processing Systems (NIPS) Foundationen_US
dc.publisher.urihttps://proceedings.neurips.cc/paper/2013/hash/c06d06da9666a219db15cf575aff2824-Abstract.htmlen_US
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofconferencenameConference on Neural Information Processing Systems 2013 (NIPS 2013)en_US
dc.relation.ispartofconferencetitleAdvances in Neural Information Processing Systems 26 (NIPS 2013)en_US
dc.relation.ispartofdatefrom2013-12-05
dc.relation.ispartofdateto2013-12-08
dc.relation.ispartoflocationLake Tahoe, United Statesen_US
dc.rights.retentionY
dc.subject.fieldofresearchArtificial Intelligence and Image Processing not elsewhere classifieden_US
dc.subject.fieldofresearchcode080199en_US
dc.titleLearning Chordal Markov Networks by Constraint Satisfactionen_US
dc.typeConference outputen_US
dc.type.descriptionE2 - Conferences (Non Refereed)en_US
dc.type.codeE - Conference Publicationsen_US
dc.description.versionVersion of Record (VoR)en_US
gro.rights.copyright© The Author(s) 2013. The attached file is reproduced here in accordance with the copyright policy of the publisher. For information about this conference please refer to the conference’s website or contact the author(s).en_US
gro.hasfulltextFull Text
gro.griffith.authorRintanen, Jussi


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    Contains papers delivered by Griffith authors at national and international conferences.

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