A randomized approximation of the MDL for stochastic models with hidden variables

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A randomized approximation of the MDL for stochastic models with hidden variables

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

Desenzano del Garda, Italy

Pages: 99 - 109

Year of Publication: 1996

ISBN:0-89791-811-8

Author

Kenji Yamanishi

C&C Research Laboratories, NEC Corporation, 1-1, 4-chome, Miyazaki, Miyamae-ku, Kawasaki, Kanagawa 216, Japan

Sponsors

Univ degli Studi de Milano : Universite degli Studi de Milano
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGART: ACM Special Interest Group on Artificial Intelligence

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 17, Downloads (12 Months): 32, Citation Count: 2

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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.

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CITED BY 2

	Hang Li , Kenji Yamanishi, Document classification using a finite mixture model, Proceedings of the 35th annual meeting on Association for Computational Linguistics, p.39-47, July 07-12, 1997, Madrid, Spain
	Kenji Yamanishi, Distributed cooperative Bayesian learning strategies, Proceedings of the tenth annual conference on Computational learning theory, p.250-262, July 06-09, 1997, Nashville, Tennessee, United States