Online learning of Bayesian network parameters with incomplete data

Sungsoo Lim, Sung Bae Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationComputational Intelligence International Conference on Intelligent Computing, ICIC 2006, Proceedings
PublisherSpringer Verlag
Number of pages6
ISBN (Print)3540372741, 9783540372745
Publication statusPublished - 2006
EventInternational Conference on Intelligent Computing, ICIC 2006 - Kunming, China
Duration: 2006 Aug 162006 Aug 19

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4114 LNAI - II
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


OtherInternational Conference on Intelligent Computing, ICIC 2006

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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