Exploiting Tractable Substructures in Intractable Networks

NIPS 1995: Proceedings of the 8th International Conference on Neural Information Processing Systems

Volumn , Issue , 1995, Pages 486-492

  Saul, Lawrence K ^a   Jordan, Michael I ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DEGREES OF FREEDOM (MECHANICS); HIDDEN MARKOV MODELS; NEURAL NETWORKS;

FIRST ORDER; FIRST-ORDER HIDDEN MARKOV MODELS; HIGH-ORDER; HIGHER-ORDER; MEAN FIELD APPROXIMATION; MEAN-FIELD THEORIES; NEURAL-NETWORKS; ORDERING STRUCTURE; PROBABILISTICS;

MEAN FIELD THEORY;

EID: 85156241149     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (9)

1. A. Dempster, N. Laird, and D. Rubin. (1977) Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc. B39:l-38.
2. B. H. Juang and L. R. Rabiner. (1991) Hidden Markov models for speech recognition, Tecknometrics 33: 251-272.
3. S. Luttrell. (1989) The Gibbs machine applied to hidden Markov model problems. Royal Signais and Radar Establishment: SP Research Note 99.
4. G. Parisi. (1988) Statistical field theory. Addison-Wesley: Redwood City, CA.
5. C. Peterson and J. R. Anderson. (1987) A mean field theory learning algorithm for neural networks. Complex Systems 1:995-1019.
6. L. Saul and M. Jordan. (1994) Learning in Boltzmann trees. Neural Comp. 6: 1174-1184.
7. L. Saul and M. Jordan. (1995) Boltzmann chains and hidden Markov models. In G. Tesauro, D. Touretzky, and T. Leen, eds. Advances in Neural Information Processing Systems 7. MIT Press: Cambridge, MA.
8. P. Stolorz. (1994) Recursive approaches to the statistical physics of lattice proteins. In L. Hunter, ed. Proc. 27th Hawaii Intl. Conf. on System Sciences V: 316-325.
9. C. Williams and G. E. Hinton. (1990) Mean field networks that learn to discriminate temporally distorted strings. Proc. Connectionist Models Summer School: 18-22.