Distributed cooperative Bayesian learning strategies

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Distributed cooperative Bayesian learning strategies

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Annual Workshop on Computational Learning Theory archive
Proceedings of the tenth annual conference on Computational learning theory table of contents

Nashville, Tennessee, United States

Pages: 250 - 262

Year of Publication: 1997

ISBN:0-89791-891-6

Author

Kenji Yamanishi

Theory NEC Laboratory, Real World Computing Partnership, c/o C&C Research Laboratories, NEC Corporation, 1-1, 4-chome, Miyazaki, Miyamae-ku, Kawasaki, Kanagawa 216, Japan

Sponsors

AT&T Labs :
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGART: ACM Special Interest Group on Artificial Intelligence
Vanderbilt University : Vanderbilt University

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 5, Downloads (12 Months): 37, Citation Count: 4

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REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	J.O. Berger, Statistical Decision Theory and Bayesian Analysis, Springer-Verlag, 1985.
	2	B.S. Clarke and A.R. Barron, "Informationtheoretic asymptotics of Bayes methods," IEEE Trans. Inform. Theory, IT-36, pp.453-471, 1990.
	3	A.E. Gelfand and A.F.M. Smith, "Sampling-based approach to calculating marginal densities," J. Am. Statist. Assoc., vol. 85, pp.398-409, 1990.
	4	S. Geman and D. Geman, "Stochastic relaxation, Gibbs distributions, and the Bayes restoration of images," IEEE Trans. on Pattern Analysis and Machine Intelligence, PAMI-6, pp.721-741, 1984.
	5	W.K. Hastings, "Monte Carlo sampling method using Markov chains and their applications," Biometrika, vol. 57, pp.97-109, 1970.
	6	David Haussler , Jyrki Kivinen , Manfred K. Warmuth, Tight worst-case loss bounds for predicting with expert advice, Proceedings of the Second European Conference on Computational Learning Theory, p.69-83, March 13-15, 1995
	7	Michael Kearns , H. Sebastian Seung, Learning from a population of hypotheses, Proceedings of the sixth annual conference on Computational learning theory, p.101-110, July 26-28, 1993, Santa Cruz, California, United States [doi>10.1145/168304.168317]
	8	Atsuyoshi Nakamura , Naoki Abe , Jun-ichi Takeuchi, Efficient Distribution-free Population Learning of Simple Concepts, Proceedings of the 4th International Workshop on Analogical and Inductive Inference: Algorithmic Learning Theory, p.500-515, October 10-15, 1994
	9	E. Nummelin, General irreducible Markov chains and non-negative operators, Cambridge University Press, 1984.
	10	J. Rosenthal, "Rates of convergence for Gibbs sampling for variance component models," Technical report No.9322, Univ. of Toronto, Dept. of Statistics, 1993.
	11	Alistair Sinclair, Algorithms for random generation and counting: a Markov chain approach, Birkhauser Verlag, Basel, Switzerland, 1993
	12	M.A. Tanner and H.W. Wong, "The calculation of posterior distributions by data augmentation," Jr. American Statist. Assoc., vol.82, pp.528-550, 1987.
	13	Volodimir G. Vovk, Aggregating strategies, Proceedings of the third annual workshop on Computational learning theory, p.371-386, August 06-08, 1990, Rochester, New York, United States
	14	K. Yamanishi, "A decision-theoretic extension of stochastic complexity and its approximation to learning," submitted for publication, 1995.
	15	Kenji Yamanishi, A randomized approximation of the MDL for stochastic models with hidden variables, Proceedings of the ninth annual conference on Computational learning theory, p.99-109, June 28-July 01, 1996, Desenzano del Garda, Italy [doi>10.1145/238061.238074]

CITED BY 4

	Kenji Yamanishi, Minimax relative loss analysis for sequential prediction algorithms using parametric hypotheses, Proceedings of the eleventh annual conference on Computational learning theory, p.32-43, July 24-26, 1998, Madison, Wisconsin, United States
	Tao Li , Shenghuo Zhu , Mitsunori Ogihara, A new distributed data mining model based on similarity, Proceedings of the 2003 ACM symposium on Applied computing, March 09-12, 2003, Melbourne, Florida
	B. Park , H. Kargupta , E. Johnson , E. Sanseverino , D. Hershberger , L. Silvestre, Distributed, Collaborative Data Analysis from Heterogeneous Sites Using a Scalable Evolutionary Technique, Applied Intelligence, v.16 n.1, p.19-42, January-February 2002
	Sachin Shetty , Min Song , Houjun Yang , Lisa Matthews, Learning distributed bayesian network structure using majority-based method, Journal of Computational Methods in Sciences and Engineering, v.9 n.1,2S1, p.53-68, April 2009