On sequential prediction of individual sequences relative to a set of experts

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

On sequential prediction of individual sequences relative to a set of experts

Full text

Pdf (1.09 MB)

Source

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

Madison, Wisconsin, United States

Pages: 1 - 11

Year of Publication: 1998

ISBN:1-58113-057-0

Authors

Nicolò Cesa-Bianchi	Department of Information Sciences, University of Milan, Via Comelico 39, 20135 Milano, Italy
Gábor Lugosi	Department of Economics, Pompeu Fabra University, Ramon Trias Fargas 25-27, 08005 Barcelona, Spain

Sponsors

University of Wisconsin : University of Wisconsin
UC @ Santa Cruz : UC @ Santa Cruz
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGART: ACM Special Interest Group on Artificial Intelligence

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 1, Downloads (12 Months): 10, Citation Count: 1

Additional Information:

references cited by index terms collaborative colleagues

Tools and Actions:	Review this Article Save this Article to a Binder Display Formats: BibTeX EndNote ACM Ref

DOI Bookmark:	Use this link to bookmark this Article: http://doi.acm.org/10.1145/279943.279946 What is a DOI?

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	K. Azuma. Weighted sums of certain dependent random variables. Tohoku Mathematical Journal, 68:357- 367, 1967.
	2	Nicolò Cesa-Bianchi , Yoav Freund , David Haussler , David P. Helmbold , Robert E. Schapire , Manfred K. Warmuth, How to use expert advice, Journal of the ACM (JACM), v.44 n.3, p.427-485, May 1997 [doi>10.1145/258128.258179]
	3	Nicolò Cesa-Bianchi, Analysis of two gradient-based algorithms for on-line regression, Proceedings of the tenth annual conference on Computational learning theory, p.163-170, July 06-09, 1997, Nashville, Tennessee, United States [doi>10.1145/267460.267492]
	4	Y.S. Chow and H. Teicher. Probability Theory, Independence, Interchangeability, Martingales. Springer- Verlag, New York, 1978.
	5	Thomas H. Chung, Minimax learning in iterated games via distributional majorization, Stanford University, Stanford, CA, 1994
	6	T.M. Cover. Behavior of sequential predictors of binary sequences. In Proceedings of the 4th Prague Conference on Information Theory, Statistical Decision Functions, Random Processes, pages 263-272. Publishing House of the Czechoslovak Academy of Sciences, Prague, 1965.
	7	L. Devroye, L. Gy6rfi, and G. Lugosi. A Probabilistic Theory of Pattern Recognition. Springer-Verlag, New York, 1996.
	8	M. Feder, N. Merhav, and M. Gutman. Universal prediction of individual sequences. IEEE Transactions on Information Theory, 38:1258-1270, 1992.
	9	E.N. Gilbert. A comparison of signalling alphabets. Bell System Technical Journal, 31:504-522, 1952.
	10	W. Hoeffding. Probability inequalities for sums of bounded random variables. Journal of the American Statistical Association, 58:13-30, 1963.
	11	M. Ledoux and M. Talagrand. Probability in Banach Space. Springer-Verlag, New York, 1991.
	12	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]
	13	C. McDiarmid. On the method of bounded differences. In Surveys in Combinatorics 1989, pages 148- 188. Cambridge University Press, Cambridge, 1989.
	14	M. Opper and D. Haussler. Worst case prediction over sequences under log loss. In The Mathematics oflnformation Coding, Extraction and Distribution, Springer- Verlag, New York, 1998.
	15	D. Pollard. Asymptotics via empirical processes. Statistical Science, 4:341-366, 1989.
	16	S.J. Szarek. On the best constants in the Khintchine inequality. Studia Mathematica, 63:197-208, 1976.
	17	M. Talagrand. Majorizing measures: the generic chaining. Annals of Probability, 24:1049-1103, 1996. (Special Invited Paper).
	18	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