On probably correct classification of concepts

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

On probably correct classification of concepts

Full text

Pdf (584 KB)

Source

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: 111 - 116

Year of Publication: 1993

ISBN:0-89791-611-5

Authors

Sanjeev R. Kulkarni
Ofer Zeitouni

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): 0, Downloads (12 Months): 12, Citation Count: 3

Additional Information:

references cited by index terms collaborative colleagues

Tools and Actions:	Review this Article Save this Article to a Binder Display Formats: BibTeX EndNote ACM Ref

DOI Bookmark:	Use this link to bookmark this Article: http://doi.acm.org/10.1145/168304.168318 What is a DOI?

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.

	BD9l	S. Ben-David. Can finite samples detect singularities of real-valued functions? Preprint, 1991.
	BI91	Gyora M. Benedek , Alon Itai, Learnability with respect to fixed distributions, Theoretical Computer Science, v.86 n.2, p.377-389, Sept. 2, 1991 [doi>10.1016/0304-3975(91)90026-X]
	Cov73	T.M. Cover. On determining the irrationality of the mean of a random variable. Ann. Star., 1:862-871, 1973.
	DP91	A. Dembo and Y. Peres. A topological criterionfor hypothesis testing. Preprint, 1991. To appear, Annals of Statistics.
	DZ92	A. Dembo and O. Zeltouni. Large Deviations Techniques and Applications. Boston: Jones and Bartlett, 1992.
	Kea92	M.J. Kearns. Oblivious pac learning of concept hierarchies. Preprint, 1992.
	KMTZ92	S. R. Kulkarni , J. N. Tsitsiklis , S. K. Mitter , O. Zeitouni, PAC learning with generalized samples and an application to stochastic geometry, Proceedings of the fifth annual workshop on Computational learning theory, p.172-179, July 27-29, 1992, Pittsburgh, Pennsylvania, United States [doi>10.1145/130385.130405]
	Kop77	J. Koplowitz. Abstracts of papers. Int. Syrup. on Info. Theory, Oct. 10-14, 1977.
	KT92	S.R. Kulkarni and D.N.C. Tse. A paradigm for class identification problems. In Proc. Princeton Conference on Information Sciences and Systems, pages 442-447, 1992.
	KZ91	S.R. Kulkarni and O. Zeitouni. Can one decide the type of the mean from the empirical measure? Star. E4 Prob. Letters, 12:323-327, 1991.
	TDT	D. Tse, M.A. Dahleh, and J.N. Tsitsiklis. Optimal asymptotic identification under bounded disturbances. To appear in IEEE Trans. Auto. Control.
	WS92	Rolf Wiehagen , Carl H. Smith, Generalization versus classification, Proceedings of the fifth annual workshop on Computational learning theory, p.224-230, July 27-29, 1992, Pittsburgh, Pennsylvania, United States [doi>10.1145/130385.130410]
	Yam92	Kenji Yamanishi, Probably almost discriminative learning, Proceedings of the fifth annual workshop on Computational learning theory, p.164-171, July 27-29, 1992, Pittsburgh, Pennsylvania, United States [doi>10.1145/130385.130404]
	ZG91	O. Zeitouni and M. Gutman. On universal hypothesis testing via large deviations. IEEE ~rans. Info. Theory, IT-37:285-290, Errata p. 698, 1991.
	ZK91	O. Zeitouni and S.R. Kullmrni. A general classification rule for probability measures. 1991. Submitted.

CITED BY 3

	Michael Kearns , Dana Ron, Testing problems with sub-learning sample complexity, Proceedings of the eleventh annual conference on Computational learning theory, p.268-279, July 24-26, 1998, Madison, Wisconsin, United States
	Oded Goldreich , Shari Goldwasser , Dana Ron, Property testing and its connection to learning and approximation, Journal of the ACM (JACM), v.45 n.4, p.653-750, July 1998
	Dana Ron, Property Testing: A Learning Theory Perspective, Foundations and Trends® in Machine Learning, v.1 n.3, p.307-402, March 2008