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): 5, Downloads (12 Months): 23, 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.

	AW98	Peter Auer , Manfred K. Warmuth, Tracking the Best Disjunction, Machine Learning, v.32 n.2, p.127-150, Aug. 1998 [doi>10.1023/A:1007472513967]
	BB97	Avrim Blum , Carl Burch, On-line learning and the metrical task system problem, Proceedings of the tenth annual conference on Computational learning theory, p.45-53, July 06-09, 1997, Nashville, Tennessee, United States [doi>10.1145/267460.267475]
	Bre67	L.M. Bregman. The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming. USSR Computational Mathematics and Physics, 7:200-217, 1967.
	Byl97	Tom Bylander, The binary exponentiated gradient algorithm for learning linear functions, Proceedings of the tenth annual conference on Computational learning theory, p.184-192, July 06-09, 1997, Nashville, Tennessee, United States [doi>10.1145/267460.267495]
	CBLW96	N. Cesa-Bianchi, P. Long, and M.K. Warmuth. Worst-case quadratic loss bounds for on-line prediction of linear functions by gradient descent. IEEE Transactions on Neural Networks, 7(2):604-619, May 1996.
	CL81	Y. Censor and A. Lent. An iterative row-action method for interval convex programming. Journal of Optimization Theory and Applications, 4(3):321-353, July 1981.
	Cov91	T.M. Coven Universal portfolios. Mathematical Finance, 1(1):1-29, 1991.
	Csi91	Imre Csiszar. Why least squares and maximum entropy? An axiomatic approach for linar inverse problems. The Annals of Statistics, 19(4):2032-2066, 1991.
	FS97	Yoav Freund , Robert E. Schapire, A decision-theoretic generalization of on-line learning and an application to boosting, Journal of Computer and System Sciences, v.55 n.1, p.119-139, Aug. 1997 [doi>10.1006/jcss.1997.1504]
	GLS97	Adam J. Grove , Nick Littlestone , Dale Schuurmans, General convergence results for linear discriminant updates, Proceedings of the tenth annual conference on Computational learning theory, p.171-183, July 06-09, 1997, Nashville, Tennessee, United States [doi>10.1145/267460.267493]
	HKW95	D. P. Helmbold, J. Kivinen, and M. K. Warmuth. Worst-case loss bounds for sigmoided linear neurons. In Proc. 1995 Neural Information Processing Conference, pages 309-315. MIT Press, Cambridge, MA, November 1995.
	HKW97	D. Haussler, J. Kivinen, and M. K. Warmuth. Tight worst-case loss bounds for predicting with expert advice. IEEE Transactions on Information Theory, 1997. To appear.
	HLS96	David P. Helmbold , Darrell D. E. Long , Bruce Sherrod, A dynamic disk spin-down technique for mobile computing, Proceedings of the 2nd annual international conference on Mobile computing and networking, p.130-142, November 1996, Rye, New York, United States [doi>10.1145/236387.236423]
	HSSW96	D. Helmbold, R. E. Schapire, Y. Singer, and M. K. Warmuth. On-line portfolio selection usng multiplicative updates. In Proc. 13th International Conference on Machine Learning, pages 243-251. Morgan Kaufmann, San Francisco, July 1996.
	HW98	Mark Herbster , Manfred K. Warmuth, Tracking the Best Expert, Machine Learning, v.32 n.2, p.151-178, Aug. 1998 [doi>10.1023/A:1007424614876]
	JB90	L. Jones and C. Byme. General entropy criteria for inverse problems, with applications to data compression, pattern classification and cluster analysis. IEEE Transactions on Information Theory, 36(1):23-30, 1990.
	JW98	A. Jagota and M. K. Warmuth. Continuous and discrete time nonlinear gradient descent: relative loss bounds and convergence. In R. Greiner E. Boros, editor, Electronic Proceedings of Fifth International Symposium on Artificial Intelligence and Mathematics, pages - Electronic,http://rutcor. rutgers.edu?amai, 1998.
	KW94	Jyrki Kivinen , Manfred K. Warmuth, Using experts for predicting continuous outcomes, Proceedings of the first European conference on Computational learning theory, p.109-120, October 1994, Royal Holloway, Univ. of London, United Kingdom
	KW97a	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]
	KW97b	Jyrki Kivinen , Manfred K. Warmuth, Relative loss bounds for multidimensional regression problems, Proceedings of the 1997 conference on Advances in neural information processing systems 10, p.287-293, July 1998, Denver, Colorado, United States
	Lit88	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]
	Lit89	N. Littlestone, Mistake bounds and logarithmic linear-threshold learning algorithms, University of California at Santa Cruz, Santa Cruz, CA, 1990
	LW94	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]
	Roc70	R. Rockafellar. Convex Analysis. Princeton Univ ersity Press, 1970.
	Rud91	W. Rudin. Functional Analysis. McGraw-Hill, 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
	Vov95	V. G. Vovk, A game of prediction with expert advice, Proceedings of the eighth annual conference on Computational learning theory, p.51-60, July 05-08, 1995, Santa Cruz, California, United States [doi>10.1145/225298.225304]
	Vov97	V. Vovk, Derandomizing stochastic prediction strategies, Proceedings of the tenth annual conference on Computational learning theory, p.32-44, July 06-09, 1997, Nashville, Tennessee, United States [doi>10.1145/267460.267473]

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