Tracking the best regressor

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Tracking the best regressor

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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: 24 - 31

Year of Publication: 1998

ISBN:1-58113-057-0

Authors

Mark Herbster	Department of Computer Science, University of California at Santa Cruz, Applied Sciences Building, Santa Cruz, CA
Manfred K. Warmuth	Department of Computer Science, University of California at Santa Cruz, Applied Sciences Building, Santa Cruz, CA

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): 7, Downloads (12 Months): 21, Citation Count: 10

Additional Information:

references cited by index terms collaborative colleagues

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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.

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CITED BY 10

	Katy S. Azoury , M. K. Warmuth, Relative Loss Bounds for On-Line Density Estimation with the Exponential Family of Distributions, Machine Learning, v.43 n.3, p.211-246, June 2001
	Jürgen Forster , Manfred K. Warmuth, Relative Loss Bounds for Temporal-Difference Learning, Machine Learning, v.51 n.1, p.23-50, April 2003
	John Lafferty, Additive models, boosting, and inference for generalized divergences, Proceedings of the twelfth annual conference on Computational learning theory, p.125-133, July 07-09, 1999, Santa Cruz, California, United States
	Philip M. Long, The complexity of learning according to two models of a drifting environment, Proceedings of the eleventh annual conference on Computational learning theory, p.116-125, July 24-26, 1998, Madison, Wisconsin, United States
	Claudio Gentile , Nick Littlestone, The robustness of the p-norm algorithms, Proceedings of the twelfth annual conference on Computational learning theory, p.1-11, July 07-09, 1999, Santa Cruz, California, United States
	Jyrki Kivinen , Manfred K. Warmuth, Boosting as entropy projection, Proceedings of the twelfth annual conference on Computational learning theory, p.134-144, July 07-09, 1999, Santa Cruz, California, United States
	Claudio Gentile, The Robustness of the p-Norm Algorithms, Machine Learning, v.53 n.3, p.265-299, December 2003
	Philip M. Long, The Complexity of Learning According to Two Models of a Drifting Environment, Machine Learning, v.37 n.3, p.337-354, Dec. 1999
	Mark Herbster , Manfred K. Warmuth, Tracking the best linear predictor, The Journal of Machine Learning Research, 1, p.281-309, 9/1/2001
	Frank Nielsen , Jean-Daniel Boissonnat , Richard Nock, On Bregman Voronoi diagrams, Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, p.746-755, January 07-09, 2007, New Orleans, Louisiana