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.
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Britos, P., Felgaer, P., Garcia-Martinez, R. (2008). Bayesian Networks Optimization Based on Induction Learning Techniques. In: Bramer, M. (eds) Artificial Intelligence in Theory and Practice II. IFIP AI 2008. IFIP – The International Federation for Information Processing, vol 276. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09695-7_44
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DOI: https://doi.org/10.1007/978-0-387-09695-7_44
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-09694-0
Online ISBN: 978-0-387-09695-7
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