dc.contributor.author | Corander, Jukka | |
dc.contributor.author | Janhunen, Tomi | |
dc.contributor.author | Rintanen, Jussi | |
dc.contributor.author | Nyman, Henrik | |
dc.contributor.author | Pensar, Johan | |
dc.date.accessioned | 2022-11-23T00:17:28Z | |
dc.date.available | 2022-11-23T00:17:28Z | |
dc.date.issued | 2013 | |
dc.date.modified | 2014-07-07T22:14:17Z | |
dc.identifier.isbn | 9781632660244 | en_US |
dc.identifier.uri | http://hdl.handle.net/10072/61104 | |
dc.description.abstract | We 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.publicationstatus | Yes | |
dc.language | English | en_US |
dc.publisher | Neural Information Processing Systems (NIPS) Foundation | en_US |
dc.publisher.uri | https://proceedings.neurips.cc/paper/2013/hash/c06d06da9666a219db15cf575aff2824-Abstract.html | en_US |
dc.relation.ispartofstudentpublication | N | |
dc.relation.ispartofconferencename | Conference on Neural Information Processing Systems 2013 (NIPS 2013) | en_US |
dc.relation.ispartofconferencetitle | Advances in Neural Information Processing Systems 26 (NIPS 2013) | en_US |
dc.relation.ispartofdatefrom | 2013-12-05 | |
dc.relation.ispartofdateto | 2013-12-08 | |
dc.relation.ispartoflocation | Lake Tahoe, United States | en_US |
dc.rights.retention | Y | |
dc.subject.fieldofresearch | Artificial Intelligence and Image Processing not elsewhere classified | en_US |
dc.subject.fieldofresearchcode | 080199 | en_US |
dc.title | Learning Chordal Markov Networks by Constraint Satisfaction | en_US |
dc.type | Conference output | en_US |
dc.type.description | E2 - Conferences (Non Refereed) | en_US |
dc.type.code | E - Conference Publications | en_US |
dc.description.version | Version 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.hasfulltext | Full Text | |
gro.griffith.author | Rintanen, Jussi | |