On learning in the limit and non-uniform (&egr;,&dgr;)-learning

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On learning in the limit and non-uniform (&egr;,&dgr;)-learning

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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: 209 - 217

Year of Publication: 1993

ISBN:0-89791-611-5

Authors

Shai Ben-David
Michal Jacovi

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

Bibliometrics

Downloads (6 Weeks): 15, Downloads (12 Months): 28, 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.

	BD92	Shai Ben-David, Can finite samples detect singularities of real-valued functions?, Proceedings of the twenty-fourth annual ACM symposium on Theory of computing, p.390-399, May 04-06, 1992, Victoria, British Columbia, Canada [doi>10.1145/129712.129749]
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	BI88	Gyora M. Benedek , Alon Itai, Nonuniform Learnability, Proceedings of the 15th International Colloquium on Automata, Languages and Programming, p.82-92, July 11-15, 1988
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	Hau92	David Haussler, DECISION THEORETIC GENERALIZATIONS OF THE PAC MODEL FORNEURAL NET AND OTHER LEARNING APPLICATIONS, University of California at Santa Cruz, Santa Cruz, CA, 1990
	Hoe63	W. Hoeffding. Probability Inequalities for Sums of Bounded Random Vanables. The American $tatzst~cal Assoczatzon, 58:13-30, 1963.
	KB92	Syam Kapur , Gianfranco Bilardi, Language learning from stochastic input, Proceedings of the fifth annual workshop on Computational learning theory, p.303-310, July 27-29, 1992, Pittsburgh, Pennsylvania, United States [doi>10.1145/130385.130419]
	KS90	M.J. Kearns and R.E. Schaplre. Efficient Distribution-free Learning of Probabilistic Concepts. In Proceedrags of the ~lst Annual Sympostum ou ~oundatwns of Computer Sczence, pages 382-391, 1990.
	LMR88	N. Linial, Y. Mansour, and R Rivest Results on Learning and the Vapnik- Chervonenkis dimensiom In Proceedrags of the 29th $ymposzum on Foundatwns of Computer Sczence, pages 120- 129, 1988.
	Ris85	J . Rissanen. Minimum Description Lenght Principle. Encyclopedza of $tatzst2cal Sczences, 5:523-527, 1985.
	Rud85	S. Rudich. Inferring the Structure of a Markov Chain from Its Output. In Proccedmga of the 28ih Annual Symposium on Foundatwn of Computer Sczence, pages 321-326, 1985.
	Val84	L. G. Valiant, A theory of the learnable, Communications of the ACM, v.27 n.11, p.1134-1142, Nov. 1984 [doi>10.1145/1968.1972]

CITED BY 2

	Wray Buntine, A Guide to the Literature on Learning Probabilistic Networks from Data, IEEE Transactions on Knowledge and Data Engineering, v.8 n.2, p.195-210, April 1996
	Eric Martin , Arun Sharma , Frank Stephan, On ordinal VC-dimension and some notions of complexity, Theoretical Computer Science, v.364 n.1, p.62-76, 2 November 2006