Sample sizes for sigmoidal neural networks

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Sample sizes for sigmoidal neural networks

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

Santa Cruz, California, United States

Pages: 258 - 264

Year of Publication: 1995

ISBN:0-89791-723-5

Author

John Shawe-Taylor

Department of Computer Science, Royal Holloway, University of London, Egham, Surrey TW20 0EX, UK

Sponsors

SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGART: ACM Special Interest Group on Artificial Intelligence
University of California : University of California

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 4, Downloads (12 Months): 17, Citation Count: 1

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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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	BEHW89	Anselm Blumer , A. Ehrenfeucht , David Haussler , Manfred K. Warmuth, Learnability and the Vapnik-Chervonenkis dimension, Journal of the ACM (JACM), v.36 n.4, p.929-965, Oct. 1989 [doi>10.1145/76359.76371]
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	GoJe93	Paul Goldberg , Mark Jerrum, Bounding the Vapnik-Chervonenkis dimension of concept classes parameterized by real numbers, Proceedings of the sixth annual conference on Computational learning theory, p.361-369, July 26-28, 1993, Santa Cruz, California, United States [doi>10.1145/168304.168377]
	Hir76	Hmscii, M.W. (1976), Differential Topology. New York: Springer-Verlag.
	KaMa94	Marek Karpinski , Angus Macintyre, Polynomial bounds for VC dimension of sigmoidal neural networks, Proceedings of the twenty-seventh annual ACM symposium on Theory of computing, p.200-208, May 29-June 01, 1995, Las Vegas, Nevada, United States [doi>10.1145/225058.225118]
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