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An Algorithm for Cooperative Learning of Bayesian Network Structure from Data

  • Conference paper
Computer Supported Cooperative Work in Design I (CSCWD 2004)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3168))

Abstract

Bayesian network is an important and powerful method for representing and reasoning under conditions of uncertainty, and has been widely used in artificial intelligence and knowledge engineering. Structure learning is certainly the most difficult problem in Bayesian network research. In this paper we give an introduction to Bayesian networks, and review the related work on leaning Bayesian networks. Then we discuss the major difficulties in structure learning, and propose an efficient algorithm for cooperative learning of Bayesian network structure from database. The experimental results from a case study prove that such an approach is feasible and robust.

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© 2005 Springer-Verlag Berlin Heidelberg

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Huang, J., Pan, H., Wan, Y. (2005). An Algorithm for Cooperative Learning of Bayesian Network Structure from Data. In: Shen, W., Lin, Z., Barthès, JP.A., Li, T. (eds) Computer Supported Cooperative Work in Design I. CSCWD 2004. Lecture Notes in Computer Science, vol 3168. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11568421_9

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  • DOI: https://doi.org/10.1007/11568421_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29400-9

  • Online ISBN: 978-3-540-31740-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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