Classifying learnable geometric concepts with the Vapnik-Chervonenkis dimension

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Classifying learnable geometric concepts with the Vapnik-Chervonenkis dimension

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Annual ACM Symposium on Theory of Computing archive
Proceedings of the eighteenth annual ACM symposium on Theory of computing table of contents

Berkeley, California, United States

Pages: 273 - 282

Year of Publication: 1986

ISBN:0-89791-193-8

Authors

A Blumer	University of California at Santa Cruz and Department of Mathematics and Computer Science, University of Denver, Denver, Colorado
A Ehrenfeucht	Department of Computer Science, University of Colorado, Boulder, Colorado
D Haussler	Department of Mathematics and Computer Science, University of Denver, Denver, Colorado
M Warmuth	Department of Computer and Information Sciences, University of California, Santa Cruz, California

Sponsor

SIGACT: ACM Special Interest Group on Algorithms and Computation Theory

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 7, Downloads (12 Months): 56, Citation Count: 23

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references cited by index terms collaborative colleagues

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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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	K L Clarkson, Further applications of random sampling to computational geometry, Proceedings of the eighteenth annual ACM symposium on Theory of computing, p.414-423, May 28-30, 1986, Berkeley, California, United States
	M. Kearns , M. Li , L. Pitt , L. Valiant, On the learnability of Boolean formulae, Proceedings of the nineteenth annual ACM conference on Theory of computing, p.285-295, January 1987, New York, New York, United States
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