Learning from a population of hypotheses

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Learning from a population of hypotheses

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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: 101 - 110

Year of Publication: 1993

ISBN:0-89791-611-5

Authors

Michael Kearns
H. Sebastian Seung

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): 13, Downloads (12 Months): 22, Citation Count: 4

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references cited by index terms collaborative colleagues

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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	Shun-ichi Amari , Naotake Fujita , Shigeru Shinomoto, Four types of learning curves, Neural Computation, v.4 n.4, p.605-618, July 1992 [doi>10.1162/neco.1992.4.4.605]
	2	Nicolò Cesa-Bianchi , Yoav Freund , David P. Helmbold , David Haussler , Robert E. Schapire , Manfred K. Warmuth, How to use expert advice, Proceedings of the twenty-fifth annual ACM symposium on Theory of computing, p.382-391, May 16-18, 1993, San Diego, California, United States [doi>10.1145/167088.167198]
	3	R. M. Dudley. Central limit theorems for empirical measures. The Annals of Probability, 6(6):899-929, 1978.
	4	Yoav Freund, Boosting a weak learning algorithm by majority, Proceedings of the third annual workshop on Computational learning theory, p.202-216, August 06-08, 1990, Rochester, New York, United States
	5	Yoav Freund, An improved boosting algorithm and its implications on learning complexity, Proceedings of the fifth annual workshop on Computational learning theory, p.391-398, July 27-29, 1992, Pittsburgh, Pennsylvania, United States [doi>10.1145/130385.130429]
	6	David Haussler, Decision theoretic generalizations of the PAC model for neural net and other learning applications, Information and Computation, v.100 n.1, p.78-150, Sept. 1992 [doi>10.1016/0890-5401(92)90010-D]
	7	David Haussler , Michael Kearns , Robert Schapire, Bounds on the sample complexity of Bayesian learning using information theory and the VC dimension, Proceedings of the fourth annual workshop on Computational learning theory, p.61-74, August 05-07, 1991, Santa Cruz, California, United States
	8	S. Kullback. A lower bound for discrimination information in terms of variation. IEEE Transactions on fn/ormation Theory, 13:126-127, 1967.
	9	David Pollard. Convergence of Stochastic Processes. Springer-Verlag, 1984.
	10	Robert E. Schapire, The Strength of Weak Learnability, Machine Learning, v.5 n.2, p.197-227, Jun. 1990 [doi>10.1023/A:1022648800760]
	11	H.S. Seung, H. Sompolinsky, and N. Tishby. Statistical mechanics of learning from examples. Physical Remew A, 45(8):6056-6091, April 1992.
	12	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

CITED BY 4

	Kukjin Kang , Jong-Hoon Oh, Learning by a population of perceptrons, Proceedings of the eighth annual conference on Computational learning theory, p.297-300, July 05-08, 1995, Santa Cruz, California, United States
	Kenji Yamanishi, Minimax relative loss analysis for sequential prediction algorithms using parametric hypotheses, Proceedings of the eleventh annual conference on Computational learning theory, p.32-43, July 24-26, 1998, Madison, Wisconsin, United States
	Kenji Yamanishi, Distributed cooperative Bayesian learning strategies, Proceedings of the tenth annual conference on Computational learning theory, p.250-262, July 06-09, 1997, Nashville, Tennessee, United States
	Satoshi Morinaga , Kenji Yamanishi , Jun-ichi Takeuchi, Distributed cooperative mining for information consortia, Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, August 24-27, 2003, Washington, D.C.