Worst-case quadratic loss bounds for a generalization of the Widrow-Hoff rule

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Worst-case quadratic loss bounds for a generalization of the Widrow-Hoff rule

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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: 429 - 438

Year of Publication: 1993

ISBN:0-89791-611-5

Authors

Nicolò Cesa Bianchi
Philip M. Long
Manfred K. Warmuth

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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Downloads (6 Weeks): 15, Downloads (12 Months): 27, Citation Count: 2

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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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	David D. Lewis , Robert E. Schapire , James P. Callan , Ron Papka, Training algorithms for linear text classifiers, Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval, p.298-306, August 18-22, 1996, Zurich, Switzerland
	Kim L. Blackmore , Robert C. Williamson , Iven M. Y. Mareels , William A. Sethares, Online learning via congregational gradient descent, Proceedings of the eighth annual conference on Computational learning theory, p.265-272, July 05-08, 1995, Santa Cruz, California, United States