Game theory, on-line prediction and boosting

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Game theory, on-line prediction and boosting

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

Desenzano del Garda, Italy

Pages: 325 - 332

Year of Publication: 1996

ISBN:0-89791-811-8

Authors

Yoav Freund	AT&T Laboratories, 600 Mountain Avenue, Murray Hill, NJ
Robert E. Schapire	AT&T Laboratories, 600 Mountain Avenue, Murray Hill, NJ

Sponsors

Univ degli Studi de Milano : Universite degli Studi de Milano
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGART: ACM Special Interest Group on Artificial Intelligence

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 15, Downloads (12 Months): 89, Citation Count: 27

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references cited by index terms collaborative colleagues peer to peer

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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	David Blackwell. An analog of the minimax theorem for vector payoffs. Pacific Journal of Mathematics, 6(1): 1-8, Spring 1956.
	2	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]
	3	Thomas S. Ferguson. Mathematical Statistics: A Decision Theoretic Approach. Academic Press, 1967.
	4	Dean Foster and Rakesh Vohra. Regret in on-line decision making, unpublished manuscript, 1996.
	5	Dean Foster and Rakesh V. Vohra. Asymptotic calibration. unpublished manuscript, 1995.
	6	Dean P. Foster , Rakesh V. Vohra, A randomization rule for selecting forecasts, Operations Research, v.41 n.4, p.704-709, July–Aug. 1993 [doi>10.1287/opre.41.4.704]
	7	Yoav Freund, Boosting a weak learning algorithm by majority, Information and Computation, v.121 n.2, p.256-285, Sept. 1995 [doi>10.1006/inco.1995.1136]
	8	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]
	9	Yoav Freund , Robert E. Schapire, A decision-theoretic generalization of on-line learning and an application to boosting, Proceedings of the Second European Conference on Computational Learning Theory, p.23-37, March 13-15, 1995
	10	Drew Fudenberg and David K. Levine. Consistency and cautious fictitious play. Journal of Economic Dynamics and Control, 19:1065-1089, 1995.
	11	Drew Fudenberg and Jean Tirole. Game Theory. MIT Press, 1991.
	12	Mikael Goldmann, Johan Hfistad, and Alexander Razborov. Majority gates vs. general weighted threshold gates. Computational Complexity, 2:277-300, 1992.
	13	James Hannan. Approximation to Bayes risk in repeated play. In M. Dresher, A. W. Tucker, and P. Wolfe, editors, Contributions to the Theory of Games, volume III, pages 97-139. Princeton University Press, 1957.
	14	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]
	15	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]
	16	Serge A. Plotkin , David B. Shmoys , Éva Tardos, Fast approximation algorithms for fractional packing and covering problems, Mathematics of Operations Research, v.20 n.2, p.257-301, May 1995 [doi>10.1287/moor.20.2.257]
	17	Robert E. Schapire, The Strength of Weak Learnability, Machine Learning, v.5 n.2, p.197-227, Jun. 1990 [doi>10.1023/A:1022648800760]
	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

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	Avrim Blum , Jason D. Hartline, Near-optimal online auctions, Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms, January 23-25, 2005, Vancouver, British Columbia
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	Avrim Blum , Vijay Kumar , Atri Rudra , Felix Wu, Online learning in online auctions, Theoretical Computer Science, v.324 n.2-3, p.137-146, 20 September 2004
	Jan-P. Calliess , Geoffrey J. Gordon, No-regret learning and a mechanism for distributed multiagent planning, Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems, May 12-16, 2008, Estoril, Portugal
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	Leo Breiman, Prediction games and arcing algorithms, Neural Computation, v.11 n.7, p.1493-1517, Oct. 1, 1999
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	Lance Fortnow , Russell Impagliazzo , Valentine Kabanets , Christopher Umans, On the Complexity of Succinct Zero-Sum Games, Computational Complexity, v.17 n.3, p.353-376, October 2008
	R. Servedio, PAC Analogues of Perceptron and Winnow Via Boosting the Margin, Machine Learning, v.47 n.2-3, p.133-151, May-June 2002
	Philip M. Long, Minimum majority classification and boosting, Eighteenth national conference on Artificial intelligence, p.181-186, July 28-August 01, 2002, Edmonton, Alberta, Canada
	Gilles Blanchard, Different Paradigms for Choosing Sequential Reweighting Algorithms, Neural Computation, v.16 n.4, p.811-836, April 2004
	G. Rätsch , T. Onoda , K.-R. Müller, Soft Margins for AdaBoost, Machine Learning, v.42 n.3, p.287-320, March 2001
	Holger Schwenk , Yoshua Bengio, Boosting Neural Networks, Neural Computation, v.12 n.8, p.1869-1887, August 2000
	Avrim Blum , Tuomas Sandholm , Martin Zinkevich, Online algorithms for market clearing, Journal of the ACM (JACM), v.53 n.5, p.845-879, September 2006
	Wenxin Jiang, Boosting with Noisy Data: Some Views from Statistical Theory, Neural Computation, v.16 n.4, p.789-810, April 2004
	Yijun Sun , Sinisa Todorovic , Jian Li, Increasing the Robustness of Boosting Algorithms within the Linear-programming Framework, Journal of VLSI Signal Processing Systems, v.48 n.1-2, p.5-20, August 2007
	François Fleuret, Fast Binary Feature Selection with Conditional Mutual Information, The Journal of Machine Learning Research, 5, p.1531-1555, 12/1/2004
	Gunnar Rätsch , Ayhan Demiriz , Kristin P. Bennett, Sparse Regression Ensembles in Infinite and Finite Hypothesis Spaces, Machine Learning, v.48 n.1-3, p.189-218, 2002
	Gunnar Rätsch , Sebastian Mika , Bernhard Schölkopf , Klaus-Robert Müller, Constructing Boosting Algorithms from SVMs: An Application to One-Class Classification, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.24 n.9, p.1184-1199, September 2002
	Ron Meir , Gunnar Rätsch, An introduction to boosting and leveraging, Advanced lectures on machine learning, Springer-Verlag New York, Inc., New York, NY, 2003