On-line learning with linear loss constraints

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On-line learning with linear loss constraints

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

Santa Cruz, California, United States

Pages: 412 - 421

Year of Publication: 1993

ISBN:0-89791-611-5

Authors

Nicholas Littlestone
Philip M. Long

Sponsors

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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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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	HLL92	D.P. Helmbold, N. Littlestone, and P.M. Long. Apple tasting and nearly one-sided learning. Proceedings of the 33rd Annual Symposium on the Foundations of Computer Science, 1992.
	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
	MT89	W. Maass and G. Turin. On the complexity of learning from counterexamples. Proceedings of the 30th Annual Symposium on the Foundations of Computer Science, 1989.
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	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