A comparison of new and old algorithms for a mixture estimation problem

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A comparison of new and old algorithms for a mixture estimation problem

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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: 69 - 78

Year of Publication: 1995

ISBN:0-89791-723-5

Authors

David P. Helmbold	Computer and Information Sciences, University of California, Santa Cruz, CA
Yoram Singer	Institute of Computer Science, Hebrew University, Jerusalem 91904, Israel
Robert E. Schapire	AT&T Bell Laboratories, 600 Mountain Avenue, Room 2A-424, Murray Hill, NJ
Manfred K. Warmuth	Computer and Information Sciences, University of California, Santa Cruz, CA

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): 17, Downloads (12 Months): 31, Citation Count: 5

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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 , Jun-ichi Takeuchi, Polynomial learnability of probabilistic concepts with respect to the Kullback-Leibler divergence, Proceedings of the fourth annual workshop on Computational learning theory, p.277-289, August 05-07, 1991, Santa Cruz, California, United States
	2	S. Amari. Differential Geometrical Methods in Statisitcs. Springer-Verlag, 1985.
	3	Shun-ichi Amari, The EM algorithm and information geometry in neural network learning, Neural Computation, v.7 n.1, p.13-18, Jan. 1995 [doi>10.1162/neco.1995.7.1.13]
	4	T. Cover. Universal portfolios. Mathematical Finance. 1(1):1-29, 1991.
	5	A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum-likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, B39:1-38, 1977.
	6	R. O. Duda and P. E. Hart. Pattern Classification and Scene Analysis. Wiley, 1973.
	7	G. H~ Golub and C. F. Van Loan. Matrix Computations. Johns-Hopkins University Press, 1989.
	8	Jyrki Kivinen , Manfred K. Warmuth, Additive versus exponentiated gradient updates for linear prediction, Proceedings of the twenty-seventh annual ACM symposium on Theory of computing, p.209-218, May 29-June 01, 1995, Las Vegas, Nevada, United States [doi>10.1145/225058.225121]
	9	Jyrki Kivinen , Manfred K. Warmuth, The perceptron algorithm vs. Winnow: linear vs. logarithmic mistake bounds when few input variables are relevant, Proceedings of the eighth annual conference on Computational learning theory, p.289-296, July 05-08, 1995, Santa Cruz, California, United States [doi>10.1145/225298.225333]
	10	Nick Littlestone, Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm, Machine Learning, v.2 n.4, p.285-318, April 1988 [doi>10.1023/A:1022869011914]
	11	D. G. Luenberger. Linear and Nonlinear Programming. Addison-Wesley, 1984.
	12	R. M. Neal and G. E. Hinton. A new view of the EM algorithm that justifies incremental and other variants. Unpublished manuscript, 1993.
	13	Steven J. Nowlan, Soft competitive adaptation: neural network learning algorithms based on fitting statistical mixtures, Carnegie Mellon University, Pittsburgh, PA, 1991
	14	B. C. Peters and H. F. Walker. The numerical evaluation of the maximum-likelihood estimates of a subset of mixture proportions. SIAM Journal o/ Applied Mathematics, 35:447-452, 1978.
	15	R. A. Redner and H. F. Walker. Mixture densities, maximum likelihood, and the EM algorithm. S~am Review, 26:195-239, 1984.

CITED BY 5

	Jyrki Kivinen , Manfred K. Warmuth, Additive versus exponentiated gradient updates for linear prediction, Proceedings of the twenty-seventh annual ACM symposium on Theory of computing, p.209-218, May 29-June 01, 1995, Las Vegas, Nevada, United States
	Avrim Blum , Adam Kalai, Universal portfolios with and without transaction costs, Proceedings of the tenth annual conference on Computational learning theory, p.309-313, July 06-09, 1997, Nashville, Tennessee, United States
	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
	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
	Avrim Blum , Adam Kalai, Universal Portfolios With and Without Transaction Costs, Machine Learning, v.35 n.3, p.193-205, June 1999