Average case analysis of the clipped Hebb rule for nonoverlapping perception networks

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Average case analysis of the clipped Hebb rule for nonoverlapping perception networks

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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: 151 - 157

Year of Publication: 1993

ISBN:0-89791-611-5

Authors

Mostefa Golea
Mario Marchand

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): 14, Downloads (12 Months): 27, 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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	3	Mostefa Golea , Mario Marchand , Thomas R. Hancock, On Learning µ-Perceptron Networks with Binary Weights, Advances in Neural Information Processing Systems 5, [NIPS Conference], p.591-598, November 30-December 03, 1992
	4	Gyorgyi G., "First-order Transition to Perfect Generalization in a Neural Network with Binary Synapses", Phys. Rev. A, Vol. 41, (1990), 7097- 7100.
	5	Thomas R. Hancock , Mostefa Golea , Mario Marchand, Learning Nonoverlapping Perceptron Networks from Examples and Membership Queries, Machine Learning, v.16 n.3, p.161-183, Sept. 1994 [doi>10.1023/A:1022637108202]
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