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dc.date.accessioned 2019-10-17T13:53:19Z
dc.date.available 2019-10-17T13:53:19Z
dc.date.issued 2008
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/83464
dc.description.abstract Obtaining a bayesian network from data is a learning process that is divided in two steps: structural learning and parametric learning. In this paper, we define an automatic learning method that optimizes the bayesian networks applied to classification, using a hybrid method of learning that combines the advantages of the induction techniques of the decision trees with those of the bayesian networks. en
dc.format.extent 439-443 es
dc.language en es
dc.subject mutual information es
dc.subject Bayesian networks es
dc.subject predictive capacity es
dc.subject structural learning es
dc.title Bayesian networks optimization based on induction learning techniques en
dc.type Articulo es
sedici.identifier.other doi:10.1007/978-0-387-09695-7_44 es
sedici.identifier.other eid:2-s2.0-48949120910 es
sedici.identifier.issn 1571-5736 es
sedici.creator.person Britos, Paola Verónica es
sedici.creator.person Felgaer, Pablo es
sedici.creator.person García Martínez, Ramón es
sedici.subject.materias Educación es
sedici.subject.materias Ciencias Informáticas es
sedici.description.fulltext true es
mods.originInfo.place Facultad de Informática es
sedici.subtype Articulo es
sedici.rights.license Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
sedici.rights.uri http://creativecommons.org/licenses/by-nc-sa/4.0/
sedici.description.peerReview peer-review es
sedici.relation.journalTitle IFIP International Federation for Information Processing es
sedici.relation.journalVolumeAndIssue vol. 276 es


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Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) Except where otherwise noted, this item's license is described as Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)