Bayesian networks optimization based on induction learning techniques

dc.creatorBritos, Paola Verónica
dc.creatorFelgaer, Pablo
dc.creatorGarcía Martínez, Ramón
dc.date2008
dc.date2019-10-17T13:53:19Z
dc.identifierhttp://sedici.unlp.edu.ar/handle/10915/83464
dc.identifierissn:1571-5736
dc.descriptionObtaining 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.
dc.descriptionFacultad de Informática
dc.formatapplication/pdf
dc.format439-443
dc.languageen
dc.rightshttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rightsCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.subjectEducación
dc.subjectCiencias Informáticas
dc.subjectmutual information
dc.subjectBayesian networks
dc.subjectpredictive capacity
dc.subjectstructural learning
dc.titleBayesian networks optimization based on induction learning techniques
dc.typeArticulo
dc.typeArticulo


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