Learning to model sequences generated by switching distributions

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Learning to model sequences generated by switching distributions

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

Santa Cruz, California, United States

Pages: 41 - 50

Year of Publication: 1995

ISBN:0-89791-723-5

Authors

Yoav Freund	AT&T Bell Labs, 600 Mountain Ave., Murray Hill, NJ
Dana Ron	Computer Science Institute, Hebrew University, Jerusalem, Israel

Sponsors

SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGART: ACM Special Interest Group on Artificial Intelligence
University of California : University of California

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 0, Downloads (12 Months): 8, Citation Count: 6

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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	Naoki Abe , Manfred K. Warmuth, On the Computational Complexity of Approximating Distributions by Probabilistic Automata, Machine Learning, v.9 n.2-3, p.205-260, July 1992 [doi>10.1007/BF00992677]
	2	Leonard E. Baum and J. A. Eagon. An inequality with applications to statistical estimation for probabilistic functions of markov processes and to a model for ecology. Bulletin of the American Mathematical Society, 73:360-363, 1967.
	3	Avrim Blum , Prasad Chalasani, Learning switching concepts, Proceedings of the fifth annual workshop on Computational learning theory, p.231-242, July 27-29, 1992, Pittsburgh, Pennsylvania, United States [doi>10.1145/130385.130411]
	4	Thomas M. Cover , Joy A. Thomas, Elements of information theory, Wiley-Interscience, New York, NY, 1991
	5	David Gillman , Michael Sipser, Inference and minimization of hidden Markov chains, Proceedings of the seventh annual conference on Computational learning theory, p.147-158, July 12-15, 1994, New Brunswick, New Jersey, United States [doi>10.1145/180139.181091]
	6	Nick Littlestone. Notes on the derivation of chernoff-type bounds for sums of random variables. Unpublished Manuscript, 1990.
	7	L. R. Rabiner and B. H. Juang. An introduction to hidden markov models. IEEE ASSP Magazine, 3(1):4-16, January 1986.
	8	A.J. Viterbi. Error bounds for eonvulutional codes and an asymptotically optimal decoding algorithm IEEE Trans. Inform. Theory, 13:260-269, 1967.

CITED BY 6

	Philip M. Long, The complexity of learning according to two models of a drifting environment, Proceedings of the eleventh annual conference on Computational learning theory, p.116-125, July 24-26, 1998, Madison, Wisconsin, United States
	Yoav Freund , Yishay Mansour, Estimating a mixture of two product distributions, Proceedings of the twelfth annual conference on Computational learning theory, p.53-62, July 07-09, 1999, Santa Cruz, California, United States
	Soumen Chakrabarti , Byron Dom , Piotr Indyk, Enhanced hypertext categorization using hyperlinks, ACM SIGMOD Record, v.27 n.2, p.307-318, June 1998
	Philip M. Long, The Complexity of Learning According to Two Models of a Drifting Environment, Machine Learning, v.37 n.3, p.337-354, Dec. 1999
	Soumen Chakrabarti , Sunita Sarawagi , Byron Dom, Mining Surprising Patterns Using Temporal Description Length, Proceedings of the 24rd International Conference on Very Large Data Bases, p.606-617, August 24-27, 1998
	Thomas Dean , Glenn Carroll , Richard Washington, On the prospects for building a working model of the visual cortex, Proceedings of the 22nd national conference on Artificial intelligence, p.1597-1600, July 22-26, 2007, Vancouver, British Columbia, Canada