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Online Learning of Bayesian Network Parameters with Incomplete Data

  • Sungsoo Lim
  • Sung-Bae Cho
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4114)

Abstract

Learning Bayesian network is a problem to obtain a network that is the most appropriate to training dataset based on the evaluation measures given. It is studied to decrease time and effort for designing Bayesian networks. In this paper, we propose a novel online learning method of Bayesian network parameters. It provides high flexibility through learning from incomplete data and provides high adaptability on environments through online learning. We have confirmed the performance of the proposed method through the comparison with Voting EM algorithm, which is an online parameter learning method proposed by Cohen, et al.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sungsoo Lim
    • 1
  • Sung-Bae Cho
    • 1
  1. 1.Dept. of Computer Science, Yonsei University, Shinchon-dong, Seodaemun-ku, Seoul 120-749Korea

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