An efficient extension to mixture techniques for prediction and decision trees

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An efficient extension to mixture techniques for prediction and decision trees

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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: 114 - 121

Year of Publication: 1997

ISBN:0-89791-891-6

Authors

Fernando Pereira	AT&T Labs, 600 Mountain Avenue, Murray Hill, NJ
Yoram Singer	AT&T Labs, 600 Mountain Avenue, Murray Hill, NJ

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

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Downloads (6 Weeks): 15, Downloads (12 Months): 33, 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.

	Bun90	W.L. Buntine. A Theory of Learning Classification Rules. PhD thesis, University of Technology, Sydney, 1990.
	CBFH+93	Nicolò Cesa-Bianchi , Yoav Freund , David P. Helmbold , David Haussler , Robert E. Schapire , Manfred K. Warmuth, How to use expert advice, Proceedings of the twenty-fifth annual ACM symposium on Theory of computing, p.382-391, May 16-18, 1993, San Diego, California, United States [doi>10.1145/167088.167198]
	DMW88	Alfredo DeSantis , George Markowsky , Mark N. Wegman, Learning probabilistic prediction functions, Proceedings of the first annual workshop on Computational learning theory, p.312-328, August 03-05, 1988, MIT, Cambridge, Massachusetts, United States
	FS95	Yoav Freund , Robert E. Schapire, A decision-theoretic generalization of on-line learning and an application to boosting, Journal of Computer and System Sciences, v.55 n.1, p.119-139, Aug. 1997 [doi>10.1006/jcss.1997.1504]
	Goo53	I.J. Good. The population frequencies of species and the estimation of population parameters. Biometrika, 40(3):237-264, 1953.
	HS95	David P. Helmbold , Robert E. Schapire, Predicting nearly as well as the best pruning of a decision tree, Proceedings of the eighth annual conference on Computational learning theory, p.61-68, July 05-08, 1995, Santa Cruz, California, United States [doi>10.1145/225298.225305]
	Kat87	S.M. Katz. Estimation of probabilities from sparse data for the language model component of a speech recognizer. IEEE Transactions on Acoustics Speech and Signal Processing, 35(3):400-401, 1987.
	KT81	R.E. Krichevsky and V.K. Trofimov. The performance of universal coding. IEEE Transactions on Information Theory, 27:199-207,1981.
	LW94	Nick Littlestone , Manfred K. Warmuth, The weighted majority algorithm, Information and Computation, v.108 n.2, p.212-261, Feb. 1, 1994 [doi>10.1006/inco.1994.1009]
	Ris86	J. Rissanen. Complexity of strings in the class of Markov sources. IEEE pans. inform. Theo y, 32(4):526-532, 1986.
	RL81	J. Rissanen and G.G. Langdon. Universal modeling and coding. IEEE Bans. Inform. Theory, IT-27( 1):12-23, January 1981.
	RST96	Dana Ron , Yoram Singer , Naftali Tishby, The power of amnesia: learning probabilistic automata with variable memory length, Machine Learning, v.25 n.2-3, p.117-149, Nov./Dec. 1996 [doi>10.1007/BF00114008]
	Vov90	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
	WB91	I.H. Wit,ten and T.C. Bell. The zerofrcqncncy problem: estimating the probabilities of novel events in adaptive text compression. IEEE Transactions on Information Theory, 37(4):1085 1094, 1991.
	WLZ92	M. Weinberger A. Lempel, and J. Ziv. Universal coding of finite-memory sources. IEEE Transactions on Information Theory, 38(3):1002 1014, 1992.
	WMF94	M. Weinberger, N. Merhav, and M. Feder. Optimal sequential probability assignment for individual sequence. IEEE Transactions on Information Theory, 40(2):384- 396, 1994.
	WRF95	M. Weinberger, J. Rissanen, and M. Feder. A universal finite memory source. IEEE Transactions on Information Theory, 41(3):643 652, 1995.
	WST95	F.M.J. Willems, Y.M. Shtarkov, and T.J. Tjalkens. The context tree weighting method: Basic properties. IEEE Transactions on Information Theory. 41(3):653- 664, 1995.

CITED BY 4

	David A. McAllester, PAC-Bayesian Stochastic Model Selection, Machine Learning, v.51 n.1, p.5-21, April 2003
	David A. McAllester, PAC-Bayesian model averaging, Proceedings of the twelfth annual conference on Computational learning theory, p.164-170, July 07-09, 1999, Santa Cruz, California, United States
	Eiji Takimoto , Manfred K. Warmuth, Predicting nearly as well as the best pruning of a planar decision graph, Theoretical Computer Science, v.288 n.2, p.217-235, 16 October 2002
	Eiji Takimotoa , Akira Maruokaa , Volodya Vovk, Predicting nearly as well as the best pruning of a decision tree through dynamic programming scheme, Theoretical Computer Science, v.261 n.1, p.179-209, 06/17/2001