The binary exponentiated gradient algorithm for learning linear functions

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The binary exponentiated gradient algorithm for learning linear functions

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

Nashville, Tennessee, United States

Pages: 184 - 192

Year of Publication: 1997

ISBN:0-89791-891-6

Author

Tom Bylander

Division of Computer Science, University of Texas at San Antonio, San Antonio, TX

Sponsors

AT&T Labs :
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGART: ACM Special Interest Group on Artificial Intelligence
Vanderbilt University : Vanderbilt University

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 0, Downloads (12 Months): 11, Citation Count: 7

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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.

	CBLW96	N. Cesa-Bianchi, P. M. Long, and M. K. Warmuth. Worst-case quadratic loss bounds for a generalization of the Widrow-Hoff rule. IEEE Transactions on Neural Networks, 7:604-619, 1996.
	KW94	Jyrki Kivinen , Manfred K. Warmuth, Additive versus exponentiated gradient updates for linear prediction, Proceedings of the twenty-seventh annual ACM symposium on Theory of computing, p.209-218, May 29-June 01, 1995, Las Vegas, Nevada, United States [doi>10.1145/225058.225121]
	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

CITED BY 7

	Eiji Takimoto , Manfred K. Warmuth, Path kernels and multiplicative updates, The Journal of Machine Learning Research, 4, 12/1/2003
	Olivier Bousquet , Manfred K. Warmuth, Tracking a small set of experts by mixing past posteriors, The Journal of Machine Learning Research, 3, 3/1/2003
	Mark Herbster , Manfred K. Warmuth, Tracking the best regressor, Proceedings of the eleventh annual conference on Computational learning theory, p.24-31, July 24-26, 1998, Madison, Wisconsin, 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 , 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
	Claudio Gentile, The Robustness of the p-Norm Algorithms, Machine Learning, v.53 n.3, p.265-299, December 2003
	Mark Herbster , Manfred K. Warmuth, Tracking the best linear predictor, The Journal of Machine Learning Research, 1, p.281-309, 9/1/2001