Bayesian networks optimization based on induction learning techniques
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ArticlePublication details: ref_localidad@NULL : Springer, 2008Description: 1 archivo (210,4 KB)Subject(s): Online resources: Summary: 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.
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Biblioteca Fac.Informática | A0287 (Browse shelf(Opens below)) | Available | DIF-A0287 |
Formato de archivo: PDF. -- Este documento es producción intelectual de la Facultad de Informática - UNLP (Colección BIPA/Biblioteca)
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.
Artificial Intelligence in Theory and Practice : IFIP 20th World Computer Congress, TC 12: IFIP AI 2008 Stream, September 7-10, 2008, Milano, Italy. Springer, 2008. (IFIP - The International Federation for Information Processing ; 276), pp. 439-443
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