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Volumn 44, Issue 3, 2007, Pages 412-419

Fast Bayesian network structure learning algorithm

Author keywords

Bayesian network; Branch bound technique; Conditional independence test; Minimum description length scoring

Indexed keywords

ARTIFICIAL INTELLIGENCE; DATA MINING; DATABASE SYSTEMS; DECISION SUPPORT SYSTEMS; LEARNING ALGORITHMS; PATTERN RECOGNITION;

EID: 34248651851     PISSN: 10001239     EISSN: None     Source Type: Journal    
DOI: 10.1360/crad20070307     Document Type: Article
Times cited : (5)

References (11)
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    • Learning Bayesian belief networks based on the minimum description length principle: Basic properties
    • J Suzuki. Learning Bayesian belief networks based on the minimum description length principle: Basic properties[J]. IEICE Trans on Fundamentals, 1999, E82(10): 2237-2245
    • (1999) IEICE Trans on Fundamentals , vol.E82 , Issue.10 , pp. 2237-2245
    • Suzuki, J.1
  • 5
    • 0033076357 scopus 로고    scopus 로고
    • Using evolutionary programming and minimum description length principle for data mining of Bayesian networks
    • M L Wong, W Lam, K S Leung. Using evolutionary programming and minimum description length principle for data mining of Bayesian networks[J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 1999, 21(2): 174-178
    • (1999) IEEE Trans on Pattern Analysis and Machine Intelligence , vol.21 , Issue.2 , pp. 174-178
    • Wong, M.L.1    Lam, W.2    Leung, K.S.3
  • 6
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    • 0036567524 scopus 로고    scopus 로고
    • Learning belief networks from data: An information theory based approach
    • J Cheng, R Greiner, J Kelly, et al. Learning belief networks from data: An information theory based approach[J]. Artificial Intelligence, 2002, 137(2): 43-90
    • (2002) Artificial Intelligence , vol.137 , Issue.2 , pp. 43-90
    • Cheng, J.1    Greiner, R.2    Kelly, J.3
  • 8
    • 0002219642 scopus 로고    scopus 로고
    • Learning Bayesian network structures from massive datasets: The sparse candidate algorithm
    • San Francisco, CA: Morgan Kanfmann
    • N Friedman, I Nachman, D Peer. Learning Bayesian network structures from massive datasets: The sparse candidate algorithm[C]. In: Proc of the 15th Conf on Uncertainty in Artificial Intelligence. San Francisco, CA: Morgan Kanfmann, 1999. 206-215
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    • Friedman, N.1    Nachman, I.2    Peer, D.3
  • 9
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    • A hybrid approach to discover Bayesian networks from databases using evolutionary programming
    • Maebashi, Japan
    • M L Wong, S Y Lee, K-S Leung. A hybrid approach to discover Bayesian networks from databases using evolutionary programming[C]. Int'l Conf on Data Mining ICDM, Maebashi, Japan, 2002
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    • Wong, M.L.1    Lee, S.Y.2    Leung, K.-S.3
  • 10
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    • An improved Bayesian networks learning algorithm
    • Chinese source
    • Qiang Lei, Xiao Tianyuan, Qiao Guixiu. An improved Bayesian networks learning algorithm[J]. Journal of Computer Research and Development, 2002, 39(10): 1221-1226 (in Chinese)
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    • Learning Bayesian belief networks based on the minimum description length principle: An efficient algorithm using the B and B technique
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    • Suzuki, J.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.