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Volumn , Issue , 1997, Pages 578-584

Learning Bayesian belief networks with neural network estimators

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; NEURAL NETWORKS; PROBABILITY DISTRIBUTIONS;

EID: 26444594998     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (8)

References (13)
  • 1
    • 0010635904 scopus 로고
    • An evaluation of an algorithm for inductive learning of bayesian belief networks using simulated data sets
    • San Francisco, California
    • C. Aliferis and G. F. Cooper. An evaluation of an algorithm for inductive learning of Bayesian belief networks using simulated data sets. In Proceedings of the 10th Conference of Uncertainty in AI, pages 8-14, San Francisco, California, 1994.
    • (1994) Proceedings of the 10th Conference of Uncertainty in AI , pp. 8-14
    • Aliferis, C.1    Cooper, G.F.2
  • 2
    • 0002460150 scopus 로고
    • The alarm monitoring system: A case study with two probabilistic inference techniques for belief networks
    • London, England
    • I. Beinlich, H. Suermondt, H. Chavez, and G. Cooper. The ALARM monitoring system: A case study with two probabilistic inference techniques for belief networks. In 2nd Conference of AI in Medicine Europe, pages 247-256, London, England, 1989.
    • (1989) 2nd Conference of AI in Medicine Europe , pp. 247-256
    • Beinlich, I.1    Suermondt, H.2    Chavez, H.3    Cooper, G.4
  • 5
    • 0030124955 scopus 로고    scopus 로고
    • A guide to the literature on learning probabilistic networks from data
    • To appear
    • W. Buntine. A guide to the literature on learning probabilistic networks from data. IEEE Transactions on Knowledge and Data Engineering, 1996. To appear.
    • (1996) IEEE Transactions on Knowledge and Data Engineering
    • Buntine, W.1
  • 7
    • 34249832377 scopus 로고
    • A bayesian method for the induction of probabilistic networks from data
    • G. Cooper and E. Herskovits. A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9:309-347, 1992.
    • (1992) Machine Learning , vol.9 , pp. 309-347
    • Cooper, G.1    Herskovits, E.2
  • 8
    • 0000582742 scopus 로고
    • Present position and potential developments: Some personal views. Statistical theory. The prequential approach
    • A. Dawid. Present position and potential developments: Some personal views. Statistical theory. The prequential approach. Journal of Royal Statistical Society A, 147:278-292, 1984.
    • (1984) Journal of Royal Statistical Society A , vol.147 , pp. 278-292
    • Dawid, A.1
  • 10
    • 34249761849 scopus 로고
    • Learning bayesian networks: The combination of knowledge and statistical data
    • D. Heckerman, D. Geiger, and D. Chickering. Learning Bayesian networks: the combination of knowledge and statistical data. Machine Learning, 1995.
    • (1995) Machine Learning
    • Heckerman, D.1    Geiger, D.2    Chickering, D.3
  • 11
    • 85156253956 scopus 로고
    • Discovering structure in continuous variables using bayesian networks
    • MIT Press
    • R. Hofmann and V. Tresp. Discovering structure in continuous variables using Bayesian networks. In Advances in NIPS 8. MIT Press, 1995.
    • (1995) Advances in NIPS , vol.8
    • Hofmann, R.1    Tresp, V.2
  • 12
    • 0027205884 scopus 로고
    • A scaled conjugate gradient algorithm for fast supervised learning
    • M. Moller. A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks, 6:525-533, 1993.
    • (1993) Neural Networks , vol.6 , pp. 525-533
    • Moller, M.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.