Estimating a mixture of two product distributions

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Estimating a mixture of two product distributions

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

Santa Cruz, California, United States

Pages: 53 - 62

Year of Publication: 1999

ISBN:1-58113-167-4

Authors

Yoav Freund	AT&T Labs, 180 Park Avenue, Florham Park, NJ
Yishay Mansour	AT&T Labs and Tel-Aviv University

Sponsors

SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGART: ACM Special Interest Group on Artificial Intelligence
Univ. of California, : University of California at Santa Cruz

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 6, Downloads (12 Months): 26, 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.

	CT91	Thomas M. Cover , Joy A. Thomas, Elements of information theory, Wiley-Interscience, New York, NY, 1991
	FR95	Yoav Freund , Dana Ron, Learning to model sequences generated by switching distributions, Proceedings of the eighth annual conference on Computational learning theory, p.41-50, July 05-08, 1995, Santa Cruz, California, United States [doi>10.1145/225298.225303]
	Hoe56	W. Hoeffding. On the distribution of the number of successes in independent trials. Annals of lg~amemar~ea~ Drarzs~zcs, z I: I xo- 721, 1956.
	KMR+94	Michael Kearns , Yishay Mansour , Dana Ron , Ronitt Rubinfeld , Robert E. Schapire , Linda Sellie, On the learnability of discrete distributions, Proceedings of the twenty-sixth annual ACM symposium on Theory of computing, p.273-282, May 23-25, 1994, Montreal, Quebec, Canada [doi>10.1145/195058.195155]
	TSM85	D.M. Titterington, A.F.M. Smith, and U.E. Makov. Statistical Analysis of Finite Mixture Distributions. John Wiley & Sons, t r~e~ P- 1~00.
	YS68	S.J. Yakowitz and J. D. Spragins. On the identifiability of finite mixtures. The Annals of Mathematical Statistics, 39:258-263, 1968.

CITED BY 4

	Adam J. Grove , Dan Roth, Linear Concepts and Hidden Variables, Machine Learning, v.42 n.1-2, p.123-141, January-February 2001
	François Denis , Christophe Nicolas Magnan , Liva Ralaivola, Efficient learning of Naive Bayes classifiers under class-conditional classification noise, Proceedings of the 23rd international conference on Machine learning, p.265-272, June 25-29, 2006, Pittsburgh, Pennsylvania
	Arora Sanjeev , Ravi Kannan, Learning mixtures of arbitrary gaussians, Proceedings of the thirty-third annual ACM symposium on Theory of computing, p.247-257, July 2001, Hersonissos, Greece
	S. Charles Brubaker, Robust PCA and clustering in noisy mixtures, Proceedings of the Nineteenth Annual ACM -SIAM Symposium on Discrete Algorithms, p.1078-1087, January 04-06, 2009, New York, New York