Beating the hold-out

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Beating the hold-out: bounds for K-fold and progressive cross-validation

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

Santa Cruz, California, United States

Pages: 203 - 208

Year of Publication: 1999

ISBN:1-58113-167-4

Authors

Avrim Blum	School of Computer Science, Carnegie Mellon University, Pittsburgh, PA
Adam Kalai	School of Computer Science, Carnegie Mellon University, Pittsburgh, PA
John Langford	School of Computer Science, Carnegie Mellon University, Pittsburgh, PA

Sponsors

SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGART: ACM Special Interest Group on Artificial Intelligence
Univ. of California, : University of California at Santa Cruz

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 2, Downloads (12 Months): 26, 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 and J. Spencer. The ProbabilisticMethod. Wiley, 1991.
	2	Martin Anthony , Sean B. Holden, Cross-validation for binary classification by real-valued functions: theoretical analysis, Proceedings of the eleventh annual conference on Computational learning theory, p.218-229, July 24-26, 1998, Madison, Wisconsin, United States [doi>10.1145/279943.279987]
	3	Leo Breiman, Bagging predictors, Machine Learning, v.24 n.2, p.123-140, Aug. 1996 [doi>10.1023/A:1018054314350]
	4	L. Devroye, L. Gyrofi, and G. Lugosi. A Probabilistic Theory of Pattern Recognition. Springer-Verlag, 1996.
	5	IEEE Transactions on Pattern Analysis and Machine Intelligence staff, 2003 Index, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.25 n.12, p.1645-1664, December 2003
	6	Y. Freund and R. Shapire. Discussion of the paper "Arcing classifiers" by Leo Breiman. Annals of Statistics, 26(3): 824-832, 1998.
	7	David P. Helmbold , Manfred K. Warmuth, On weak learning, Journal of Computer and System Sciences, v.50 n.3, p.551-573, June 1995 [doi>10.1006/jcss.1995.1044]
	8	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 [doi>10.1145/267460.267491]
	9	Nick Littlestone, From on-line to batch learning, Proceedings of the second annual workshop on Computational learning theory, p.269-284, December 1989, Santa Cruz, California, United States

CITED BY 3

	Matti Kääriäinen , John Langford, A comparison of tight generalization error bounds, Proceedings of the 22nd international conference on Machine learning, p.409-416, August 07-11, 2005, Bonn, Germany
	Amit Dhurandhar , Alin Dobra, Semi-analytical method for analyzing models and model selection measures based on moment analysis, ACM Transactions on Knowledge Discovery from Data (TKDD), v.3 n.1, p.1-51, March 2009
	Yoshua Bengio , Yves Grandvalet, No Unbiased Estimator of the Variance of K-Fold Cross-Validation, The Journal of Machine Learning Research, 5, p.1089-1105, 12/1/2004