Minimax regret under log loss for general classes of experts

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

Minimax regret under log loss for general classes of experts

Full text

Pdf (704 KB)

Source

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: 12 - 18

Year of Publication: 1999

ISBN:1-58113-167-4

Authors

Nicolò Cesa-Bianchi	Polo Didattico e di Ricerca, University of Milan, Via Bramante 65, 26013 Crema, Italy
Gábor Lugosi	Department of Economics, Pompeu Fabra University, Ramon Trias Fargas 25-27, 08005 Barcelona, Spain

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): 1, Downloads (12 Months): 16, Citation Count: 1

Additional Information:

references cited by index terms collaborative colleagues peer to peer

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/10.1145/307400.307407 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	A.R. Barron and Q. Xie. Asymptotic minimax loss for data compression, gambling, and prediction. Presented at an informal meeting on "On-line prediction", University of California at Santa Cruz, 1996.
	3	T. Cover. Universal portfolios. Mathematical Finance, 1:1-29, 1991.
	4	T.M. Cover and E. Ordentlich. Universal portfolios with side information. IEEE Transactions on Informaton Theory, 42(2):348-363, 1996.
	5	M. Feder. Gambling using a finite state machine. IEEE Transactions on Information Theory, 37:1459-1465, 1991.
	6	Yoav Freund, Predicting a binary sequence almost as well as the optimal biased coin, Proceedings of the ninth annual conference on Computational learning theory, p.89-98, June 28-July 01, 1996, Desenzano del Garda, Italy [doi>10.1145/238061.238072]
	7	D. Haussler and A. Barron. How well does the Bayes method work in on-line predictions of {+1,-1} values? In Proceedings of 3rd NEC Symposium, pages 74-100. SIAM, 1993.
	8	D. Haussler, J. Kivinen, and M.K. Warmuth. Sequential prediction of individual sequences under general loss functions. IEEE Transactions on Information Theory, 44:1906-1925, 1998.
	9	N. Merhav and M. Feder. Universal prediction. IEEE Transactions on Information Theory, 44(6):2124- 2147, 1998.
	10	M. Opper and D. Haussler. Worst case prediction over sequences under log loss. In The Mathematics of Information Coding, Extraction, and Distribution. Springer Verlag, 1997.
	11	J. Rissanen. Fischer information and stochastic complexity. 1EEE Transactions on Information Theory, 42:40-47, 1996.
	12	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
	13	Y.M. Shtarkov. Universal sequential coding of single messages. Translated from: Problems in Information Transmission, 23(3):3-17, 1987.
	14	M. Talagrand. Majorizing measures: the generic chaining. Annals of Probability, 24:1049-1103, 1996. (Special Invited Paper).
	15	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
	16	V. Vovk, A game of prediction with expert advice, Journal of Computer and System Sciences, v.56 n.2, p.153-173, April 1998 [doi>10.1006/jcss.1997.1556]
	17	M.J. Weinberger, N. Merhav, and M. Feder. Optimal sequential probability assignment for individual sequences. IEEE Transactions on Information Theory, 40:384-396, 1994.
	18	Kenji Yamanishi, A loss bound model for on-line stochastic prediction algorithms, Information and Computation, v.119 n.1, p.39-54, May 15, 1995 [doi>10.1006/inco.1995.1076]
	19	K. Yamanishi. A decision-theoretic extension of stochastic complexity and its application to learning. IEEE Transactions on Information Theory, 44:1424- 1440, 1998.