Regret bounds for prediction problems

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Regret bounds for prediction problems

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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: 29 - 40

Year of Publication: 1999

ISBN:1-58113-167-4

Author

Geoffrey J. Gordon

CMU Computer Science Department, 5000 Forbes Avenue, Pittsburgh, PA

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): 2, Downloads (12 Months): 19, Citation Count: 4

Additional Information:

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.

	AHW96	Peter Auer, Mark Herbster, and Manfred Warmuth. Exponentially many local minima for single neurons. In D. Touretzky, M. Mozer, and M. Hasselmo, editors, Advances in Neural Information Processing Systems, volume 8. MIT Press, 1996.
	CBFH+95	Nicolo Cesa-Bianchi , Yoav Freund , David Helmbold , David Haussler , Robert Schapire , Manfred Warmuth, HOW TO USE EXPERT ADVICE, University of California at Santa Cruz, Santa Cruz, CA, 1995
	CLR90	Thomas T. Cormen , Charles E. Leiserson , Ronald L. Rivest, Introduction to algorithms, MIT Press, Cambridge, MA, 1990
	Gor99	Geoffrey J. Gordon , Tom Mitchell, Approximate solutions to markov decision processes, 1999
	KW96	Jyrki Kivinen and Manfred K. Warmuth. Relative loss bounds for multidimensional regression problems. In D. Touretzky, M. Mozer, and M. Hasselmo, editors, Advances in Neural Information Processing Systems, volume 8. MIT Press, 1996.
	KW97	Jyrki Kivinen , Manfred K. Warmuth, Exponentiated gradient versus gradient descent for linear predictors, Information and Computation, v.132 n.1, p.1-63, Jan. 10, 1997 [doi>10.1006/inco.1996.2612]
	LW92	Nick Littlestone , Manfred K. Warmuth, THE WEIGHTED MAJORITY ALGORITHM (Supersedes 89-16), University of California at Santa Cruz, Santa Cruz, CA, 1992
	MN83	P. McCullagh and J. A. Nelder. Generalized Linear Models. Chapman & Hall, London, 2nd edition, 1983.
	OR70	James M. Ortega , Werner C. Rheinboldt, Iterative solution of nonlinear equations in several variables, Society for Industrial and Applied Mathematics, Philadelphia, PA, 2000
	Roc70	R. Tyrell Rockafellar. Convex Analysis. Princeton University Press, New Jersey, 1970.
	SO91	A. Stuart and J. K. Ord. Kendall's Advanced Theory of Statistics. Oxford University Press, New York, 5th edition, 1991.
	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

CITED BY 4

	Geoffrey J. Gordon , Amy Greenwald , Casey Marks, No-regret learning in convex games, Proceedings of the 25th international conference on Machine learning, p.360-367, July 05-09, 2008, Helsinki, Finland
	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
	Claudio Gentile, The Robustness of the p-Norm Algorithms, Machine Learning, v.53 n.3, p.265-299, December 2003
	Robert E. Mahony , Robert C. Williamson, Prior knowledge and preferential structures in gradient descent learning algorithms, The Journal of Machine Learning Research, 1, p.311-355, 9/1/2001

INDEX TERMS

Primary Classification:
I. Computing Methodologies
I.2 ARTIFICIAL INTELLIGENCE

Additional Classification:
G. Mathematics of Computing

General Terms:
Algorithms, Design, Theory, Verification

Collaborative Colleagues:

Geoffrey J. Gordon: colleagues

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