An experimental and theoretical comparison of model selection methods

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An experimental and theoretical comparison of model selection methods

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

Santa Cruz, California, United States

Pages: 21 - 30

Year of Publication: 1995

ISBN:0-89791-723-5

Authors

Michael Kearns	AT&T Bell Laboratories, Murray Hill, New Jersey
Yishay Mansour	Tel Aviv University, Tel Aviv, Israel
Andrew Y. Ng	Carnegie Mellon University, Pittsburgh, Pennsylvania
Dana Ron	Hebrew University, Jerusalem, Israel

Sponsors

SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGART: ACM Special Interest Group on Artificial Intelligence
University of California : University of California

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 1, Downloads (12 Months): 14, Citation Count: 13

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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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	2	Thomas M. Cover , Joy A. Thomas, Elements of information theory, Wiley-Interscience, New York, NY, 1991
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	8	H $. Seung, H. Sompolinsky, and N. Tishby. Statistical mechanics of leaming from examples. Physical Review, A45:6056-6091, 1992.
	9	M Stone. Cross-validatory choice and assessment of statistical predictions Journalof the Royal Statistical Society B, 36:111- 147, 1974.
	10	M. Stone~ Asymptotlcs for and against cross-validation. Biomen'ika, 64(1):29-35, 1977.
	11	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
	12	V, N. Vapnik and A. Y. Chervonenkis. On the unifoml convergence of relative frequencies of events to then: probabihties. Theory of Probability and its Applications, 16(2):264-280, 1971.

CITED BY 13

	Tom Dietterich, Overfitting and undercomputing in machine learning, ACM Computing Surveys (CSUR), v.27 n.3, p.326-327, Sept. 1995
	David A. McAllester, Some PAC-Bayesian Theorems, Machine Learning, v.37 n.3, p.355-363, Dec. 1999
	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
	Michael Kearns, A bound on the error of cross validation using the approximation and estimation rates, with consequences for the training-test split, Neural Computation, v.9 n.5, p.1143-1161, July 1, 1997
	David A. McAllester, PAC-Bayesian model averaging, Proceedings of the twelfth annual conference on Computational learning theory, p.164-170, July 07-09, 1999, Santa Cruz, California, United States
	Joel Ratsaby , Ronny Meir , Vitaly Maiorov, Towards robust model selection using estimation and approximation error bounds, Proceedings of the ninth annual conference on Computational learning theory, p.57-67, June 28-July 01, 1996, Desenzano del Garda, Italy
	Sean B. Holden, PAC-like upper bounds for the sample complexity of leave-one-out cross-validation, Proceedings of the ninth annual conference on Computational learning theory, p.41-50, June 28-July 01, 1996, Desenzano del Garda, Italy
	Michael Kearns , Dana Ron, Algorithmic stability and sanity-check bounds for leave-one-out cross-validation, Neural Computation, v.11 n.6, p.1427-1453, Aug. 15, 1999
	David A. McAllester, PAC-Bayesian Stochastic Model Selection, Machine Learning, v.51 n.1, p.5-21, April 2003
	K. -R. Müller , M. Finke , N. Murata , K. Schulten , S. Amari, A numerical study on learning curves in stochastic multilayer feedforward networks, Neural Computation, v.8 n.5, p.1085-1106, July 1, 1996
	Michael Kearns , Dana Ron, Algorithmic stability and sanity-check bounds for leave-one-out cross-validation, Proceedings of the tenth annual conference on Computational learning theory, p.152-162, July 06-09, 1997, Nashville, Tennessee, United States
	Peter L. Bartlett , Stéphane Boucheron , Gábor Lugosi, Model Selection and Error Estimation, Machine Learning, v.48 n.1-3, p.85-113, 2002
	Th. Villmann , F. Schleif , B. Hammer, Comparison of relevance learning vector quantization with other metric adaptive classification methods, Neural Networks, v.19 n.5, p.610-622, June 2006