Online learning of Bayesian network parameters with incomplete data

Sungsoo Lim; Sung Bae Cho

doi:10.1007/978-3-540-37275-2_40

Online learning of Bayesian network parameters with incomplete data

Sungsoo Lim, Sung Bae Cho

College of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

7 Citations (Scopus)

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.

Original language	English
Title of host publication	Computational Intelligence International Conference on Intelligent Computing, ICIC 2006, Proceedings
Publisher	Springer Verlag
Pages	309-314
Number of pages	6
ISBN (Print)	3540372741, 9783540372745
DOIs	https://doi.org/10.1007/978-3-540-37275-2_40
Publication status	Published - 2006
Event	International Conference on Intelligent Computing, ICIC 2006 - Kunming, China Duration: 2006 Aug 16 → 2006 Aug 19

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4114 LNAI - II
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	International Conference on Intelligent Computing, ICIC 2006
Country/Territory	China
City	Kunming
Period	06/8/16 → 06/8/19

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

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10.1007/978-3-540-37275-2_40

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Lim, S., & Cho, S. B. (2006). Online learning of Bayesian network parameters with incomplete data. In Computational Intelligence International Conference on Intelligent Computing, ICIC 2006, Proceedings (pp. 309-314). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4114 LNAI - II). Springer Verlag. https://doi.org/10.1007/978-3-540-37275-2_40

Lim, Sungsoo ; Cho, Sung Bae. / Online learning of Bayesian network parameters with incomplete data. Computational Intelligence International Conference on Intelligent Computing, ICIC 2006, Proceedings. Springer Verlag, 2006. pp. 309-314 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

author = "Sungsoo Lim and Cho, {Sung Bae}",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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note = "International Conference on Intelligent Computing, ICIC 2006 ; Conference date: 16-08-2006 Through 19-08-2006",

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Lim, S & Cho, SB 2006, Online learning of Bayesian network parameters with incomplete data. in Computational Intelligence International Conference on Intelligent Computing, ICIC 2006, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4114 LNAI - II, Springer Verlag, pp. 309-314, International Conference on Intelligent Computing, ICIC 2006, Kunming, China, 06/8/16. https://doi.org/10.1007/978-3-540-37275-2_40

Online learning of Bayesian network parameters with incomplete data. / Lim, Sungsoo; Cho, Sung Bae.
Computational Intelligence International Conference on Intelligent Computing, ICIC 2006, Proceedings. Springer Verlag, 2006. p. 309-314 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4114 LNAI - II).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - 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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Lim S, Cho SB. Online learning of Bayesian network parameters with incomplete data. In Computational Intelligence International Conference on Intelligent Computing, ICIC 2006, Proceedings. Springer Verlag. 2006. p. 309-314. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-37275-2_40

Online learning of Bayesian network parameters with incomplete data

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