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Volumn 15, Issue , 2001, Pages 91-114

Mean-field methods for a special class of belief networks

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; COMPUTER NETWORKS;

EID: 24044517971     PISSN: 10769757     EISSN: None     Source Type: Journal    
DOI: 10.1613/jair.734     Document Type: Article
Times cited : (6)

References (28)
  • 1
    • 0001578518 scopus 로고
    • A learning algorithm for boltzmann machines
    • Ackley, D., Hinton, G., & Sejnowski, T. (1985). A learning algorithm for boltzmann machines. Cognitive Science, 9, 147-169.
    • (1985) Cognitive Science , vol.9 , pp. 147-169
    • Ackley, D.1    Hinton, G.2    Sejnowski, T.3
  • 4
    • 24044536518 scopus 로고    scopus 로고
    • Mean-field theory for stochastic connectionist networks
    • Department of Computer Science and Automation, Indian Institute of Science
    • Bhattacharyya, C., & Keerthi, S. S. (1999a). Mean-field theory for stochastic connectionist networks. Tech. rep. IISc-CSA-99-03, Department of Computer Science and Automation, Indian Institute of Science.
    • (1999) Tech. Rep. IISc-CSA-99-03
    • Bhattacharyya, C.1    Keerthi, S.S.2
  • 6
    • 0034711904 scopus 로고    scopus 로고
    • Information geometry and plefka's mean-field theory
    • Bhattacharyya, C., & Keerthi, S. S. (2000). Information geometry and plefka's mean-field theory. J. Phys. A: Math. Gen., 55(7), 1307-1312.
    • (2000) J. Phys. A: Math. Gen. , vol.55 , Issue.7 , pp. 1307-1312
    • Bhattacharyya, C.1    Keerthi, S.S.2
  • 7
    • 0042761283 scopus 로고    scopus 로고
    • Approximating posterior distributions in belief networks using mixtures
    • Jordan, M. I., Kearns, M. J., & Solla, S. (Eds.), MIT press
    • Bishop, M. C., Lawrence, N., Jaakkola, T., & Jordan, M. I. (1997). Approximating posterior distributions in belief networks using mixtures. In Jordan, M. I., Kearns, M. J., & Solla, S. (Eds.), Advances in Neural Information Processing Systems 10. MIT press.
    • (1997) Advances in Neural Information Processing Systems 10
    • Bishop, M.C.1    Lawrence, N.2    Jaakkola, T.3    Jordan, M.I.4
  • 9
    • 0042138305 scopus 로고    scopus 로고
    • Model independent mean-field theory as a local method for approximate propagation of information
    • Haft, M., Hofmann, R., & Tresp, V. (1999). Model independent mean-field theory as a local method for approximate propagation of information. Network: Computation in Neural Systems, 10(1), 93-105.
    • (1999) Network: Computation in Neural Systems , vol.10 , Issue.1 , pp. 93-105
    • Haft, M.1    Hofmann, R.2    Tresp, V.3
  • 10
    • 0029652445 scopus 로고
    • The wake sleep algorithm for unsupervised neural networks
    • Hinton, G. E., Dayan, P., Frey, B., & Neal, R. (1995). The wake sleep algorithm for unsupervised neural networks. Science, 268, 1158-1161.
    • (1995) Science , vol.268 , pp. 1158-1161
    • Hinton, G.E.1    Dayan, P.2    Frey, B.3    Neal, R.4
  • 13
    • 0001022491 scopus 로고
    • Blocking gibbs sampling in very large probabilistic expert systems
    • Real World Applications of Uncertain Reasoning
    • Jensen, C., Kong, A., & Kjaerulff, U. (1995). Blocking gibbs sampling in very large probabilistic expert systems. In International Journal of Human Computer Studies. Special issue on Real World Applications of Uncertain Reasoning.
    • (1995) International Journal of Human Computer Studies , Issue.SPEC. ISSUE
    • Jensen, C.1    Kong, A.2    Kjaerulff, U.3
  • 15
    • 84899012889 scopus 로고    scopus 로고
    • Boltzmann machine learning using mean field theory and linear response correction
    • Jordan, M. I., Kearns, M. J., & Solla, S. A. (Eds.), MIT press
    • Kappen, H. J., & Rodriguez, F. B. (1998). Boltzmann machine learning using mean field theory and linear response correction. In Jordan, M. I., Kearns, M. J., & Solla, S. A. (Eds.), Advances in Neural Information Processing Systems 10. MIT press.
    • (1998) Advances in Neural Information Processing Systems 10
    • Kappen, H.J.1    Rodriguez, F.B.2
  • 17
    • 24044494162 scopus 로고    scopus 로고
    • Reduction of computational complexity in bayesian networksthrough removal of weak dependences
    • San Mateo, CA: Morgan Kaufmann
    • Kjaerulff, U. (1998). Reduction of computational complexity in bayesian networksthrough removal of weak dependences. In Uncertainity and Artificial Intelligence: Proceedings of the Tenth Conference. San Mateo, CA: Morgan Kaufmann.
    • (1998) Uncertainity and Artificial Intelligence: Proceedings of the Tenth Conference
    • Kjaerulff, U.1
  • 18
    • 0001006209 scopus 로고
    • Local computations with probabilities on graphical structures and their application to expert systems
    • Lauritzen, S., & Spiegelhalter, D. (1988). Local computations with probabilities on graphical structures and their application to expert systems. Journal of the Royal Statistical Society B, 50, 157-224.
    • (1988) Journal of the Royal Statistical Society B , vol.50 , pp. 157-224
    • Lauritzen, S.1    Spiegelhalter, D.2
  • 19
    • 44049116681 scopus 로고
    • Connectionist learning of belief networks
    • Neal, R. (1992). Connectionist learning of belief networks. Artificial Intelligence, 56, 71-118.
    • (1992) Artificial Intelligence , vol.56 , pp. 71-118
    • Neal, R.1
  • 22
    • 0001406440 scopus 로고
    • A mean field theory learning algorithm for neural networks
    • Peterson, C., & Anderson, J. R. (1987). A mean field theory learning algorithm for neural networks. Complex Systems, 1, 995-1019.
    • (1987) Complex Systems , vol.1 , pp. 995-1019
    • Peterson, C.1    Anderson, J.R.2
  • 23
    • 0001051762 scopus 로고
    • Convergence condition of the tap equation for the infinite-ranged ising glass model
    • Plefka, T. (1982). Convergence condition of the tap equation for the infinite-ranged ising glass model. J. Phys. A: Math. Gen., 15(6), 1971-1978.
    • (1982) J. Phys. A: Math. Gen. , vol.15 , Issue.6 , pp. 1971-1978
    • Plefka, T.1
  • 25
    • 0003214942 scopus 로고    scopus 로고
    • Exploiting tractable substructures in intractable networks
    • Touretzky, D. S., Mozer, M. C., & Hasselmo, M. E. (Eds.), MIT press
    • Saul, L., & Jordan, M. I. (1996). Exploiting tractable substructures in intractable networks. In Touretzky, D. S., Mozer, M. C., & Hasselmo, M. E. (Eds.), Advances in Neural Information Processing Systems 8. MIT press.
    • (1996) Advances in Neural Information Processing Systems 8
    • Saul, L.1    Jordan, M.I.2
  • 26
    • 24044460977 scopus 로고    scopus 로고
    • Attractor dynamics in feedforward networks
    • In press
    • Saul, L., & Jordan, M. I. (1999). Attractor dynamics in feedforward networks. Neural Computation. In press.
    • (1999) Neural Computation
    • Saul, L.1    Jordan, M.I.2
  • 28
    • 0026056182 scopus 로고
    • Probabilistic diagnosis using a reformulation of the internist-1/qmr knowledge base
    • Shwe, M. A., & Others (1991). Probabilistic diagnosis using a reformulation of the internist-1/qmr knowledge base. Meth. Inform. Med., 30.
    • (1991) Meth. Inform. Med. , vol.30
    • Shwe, M.A.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.