A framework for structural risk minimisation

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

A framework for structural risk minimisation

Full text

Pdf (864 KB)

Source

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: 68 - 76

Year of Publication: 1996

ISBN:0-89791-811-8

Authors

John Shawe-Taylor	Computer Science Dept, Royal Holloway University of London, Egham, TW20 0EX, UK
Peter L. Bartlett	Systems Engineering Dept, Australian National Univ, Canberra 0200 Australia
Robert C. Williamson	Engineering Dept, Australian National Univ, Canberra 0200, Australia
Martin Anthony	Mathematics Dept, London School of Economics, Houghton Street, London WC2A 2AE, UK

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): 1, Downloads (12 Months): 25, Citation Count: 9

Additional Information:

references cited by index terms collaborative colleagues

Tools and Actions:	Request Permissions 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/10.1145/238061.238070 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.

	1	Noga Alon , Shai Ben-David , Nicolò Cesa-Bianchi , David Haussler, Scale-sensitive dimensions, uniform convergence, and learnability, Journal of the ACM (JACM), v.44 n.4, p.615-631, July 1997 [doi>10.1145/263867.263927]
	2	Martin Anthony , John Shawe-Taylor, A result of Vapnik with applications, Discrete Applied Mathematics, v.47 n.3, p.207-217, Dec. 21, 1993 [doi>10.1016/0166-218X(93)90126-9]
	3	Bernhard E. Boser , Isabelle M. Guyon , Vladimir N. Vapnik, A training algorithm for optimal margin classifiers, Proceedings of the fifth annual workshop on Computational learning theory, p.144-152, July 27-29, 1992, Pittsburgh, Pennsylvania, United States [doi>10.1145/130385.130401]
	4	Corinna Cortes , Vladimir Vapnik, Support-Vector Networks, Machine Learning, v.20 n.3, p.273-297, Sept. 1995 [doi>10.1023/A:1022627411411]
	5	Nathan Linial , Yishay Mansour , Ronald L. Rivest, Results on learnability and the Vapnik-Chervonenkis dimension, Information and Computation, v.90 n.1, p.33-49, Jan. 1991 [doi>10.1016/0890-5401(91)90058-A]
	6	D. Pollard, Convergence of Stochastic Processes, Springer, New York, 1984.
	7	Vladimir Vapnik, Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics), Springer-Verlag New York, Inc., Secaucus, NJ, 1982
	8	Vladimir N. Vapnik, The nature of statistical learning theory, Springer-Verlag New York, Inc., New York, NY, 1995
	9	V.N. Vapnik and A. Ja. Chervonenkis, "On the Uniform Convergence of Relative Frequencies of Events to their Probabilities," Theory of Probability and Applications, 16, 264-280 (1971).
	10	V.N. Vapnik and A. Ja. Chervonenkis, "Ordered Risk Minimization (I and II)", Automation and Remote Control, 34, 1226-1235 and 1403-1412 (1974).

CITED BY 9

	Yoav Freund, Self bounding learning algorithms, Proceedings of the eleventh annual conference on Computational learning theory, p.247-258, July 24-26, 1998, Madison, Wisconsin, United States
	David A. McAllester, Some PAC-Bayesian theorems, Proceedings of the eleventh annual conference on Computational learning theory, p.230-234, July 24-26, 1998, Madison, Wisconsin, United States
	John Langford , Avrim Blum, Microchoice bounds and self bounding learning algorithms, Proceedings of the twelfth annual conference on Computational learning theory, p.209-214, July 07-09, 1999, Santa Cruz, California, United States
	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
	David A. McAllester, Some PAC-Bayesian Theorems, Machine Learning, v.37 n.3, p.355-363, Dec. 1999
	Sepp Hochreiter , Klaus Obermayer, Support vector machines for dyadic data, Neural Computation, v.18 n.6, p.1472-1510, June 2006
	Wei Fan , Ian Davidson, Reverse testing: an efficient framework to select amongst classifiers under sample selection bias, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, August 20-23, 2006, Philadelphia, PA, USA
	Christopher J. C. Burges, A Tutorial on Support Vector Machines for Pattern Recognition, Data Mining and Knowledge Discovery, v.2 n.2, p.121-167, June 1998
	Leonid (Aryeh) Kontorovich , Boaz Nadler, Universal Kernel-Based Learning with Applications to Regular Languages, The Journal of Machine Learning Research, 10, p.1095-1129, June 2009