A data-dependent skeleton estimate for learning

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A data-dependent skeleton estimate for learning

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

Desenzano del Garda, Italy

Pages: 51 - 56

Year of Publication: 1996

ISBN:0-89791-811-8

Authors

Gábor Lugosi	Department of Mathematics and Computer Science, Faculty of Electrical Engineering, Technical University of Budapest, 1521 Stoczek u. 2, Budapest, Hungary
Márta Pintér	Department of Mathematics and Computer Science, Faculty of Electrical Engineering, Technical University of Budapest, 1521 Stoczek u. 2, Budapest, Hungary

Sponsors

Univ degli Studi de Milano : Universite degli Studi de Milano
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): 9, Citation Count: 3

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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.

	1	N. Alon, S. Ben-David, N. Cesa-Bianchi, and D. Haussler. Scale-sensitive dimensions, uniform convergence, and learnability. In Proceedings of the 1993 IEEE Symposzum on the Foundatzons of Computer Science. IEEE Press, 1993.
	2	A. R. Barron. Complexity regularization with application to artificial neural networks. In G. Roussas, editor, Nonparametr~c Functzonal Estimation and Related Topzcs, pages 561-576. NATO ASI Series, Kluwer Academic Publishers, Dordrecht, 1991.
	3	K. L. Buescher and P. R. Kumar. Learning by canonical smooth estimation, Part II: Learning and choice of model complexity. To appear m IEEE Transactions on Automatic Control, 1994.
	4	L. Devroye, L. Gy6rfi, and G. Lugosi. A Probab~l~stzc Theory of Pattern Recogmtzon. Springer-Verlag, New York, 1996.
	5	L. Devroye and G. Lugosi. Lower bounds in pattern recognition and learning. Pattern Recogmtzon, 1996. To appear.
	6	D. Haussler, N. Littlestone, and M. Warmuth. Predicting {0, 1} functions from randomly drawn points. In Proceedings of the 29th IEEE Symposmm on the Foundatzons of Computer Sczence, pages 100- 109. IEEE Computer Society Press, Los Alamitos, CA, 1988.
	7	W. Hoeffding. Probability inequalities for sums of bounded random variables. Journal of the American Statistical Assocmtwn, 58:13-30, 1963.
	8	D. Pollard. Rates of uniform almost sure convergence for empirical processes indexed by unbounded classes of functions, 1986. Manuscript.
	9	V. N. Vapnik and A. Ya. Chervonenkis. On the uniform convergence of relative frequencies of events to their probabilities. Theory of Probabihty and ~ts AppIzcatzons, 16:264-280, 1971.

CITED BY 3

	Sandor Szedmak , John Shawe-Taylor, Synthesis of maximum margin and multiview learning using unlabeled data, Neurocomputing, v.70 n.7-9, p.1254-1264, March, 2007
	John Shawe-Taylor , Robert C. Williamson, A PAC analysis of a Bayesian estimator, Proceedings of the tenth annual conference on Computational learning theory, p.2-9, July 06-09, 1997, Nashville, Tennessee, United States
	Dale Schuurmans , Finnegan Southey, Metric-Based Methods for Adaptive Model Selection and Regularization, Machine Learning, v.48 n.1-3, p.51-84, 2002