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More theorems about scale-sensitive dimensions and learning

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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: 392 - 401

Year of Publication: 1995

ISBN:0-89791-723-5

Authors

Peter L. Bartlett	Department of Systems Engineering, RSISE. Australian National University, Canberra, 0200 Australia
Philip M. Long	Department of Computer Science, Duke University, P.O. Box 90129, Durham, NC

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): 13, Downloads (12 Months): 23, 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	N. Alon, S. Ben-David, N. Cesa-Bianchi, and D. Haussler. Scale-sensitive dimensions, uniform convergence, and learnability. In Proceedings of the 1993 IEEE Symposittm on Foundations of Computer Science. IEEE Press, 1993.
	2	Martin Anthony , Peter L. Bartlett, Function learning from interpolation, Proceedings of the Second European Conference on Computational Learning Theory, p.211-221, March 13-15, 1995
	3	Martin Anthony , Norman Biggs , John Shawe-Taylor, The learnability of formal concepts, Proceedings of the third annual workshop on Computational learning theory, p.246-257, August 06-08, 1990, Rochester, New York, United States
	4	Peter L. Bartlett , Philip M. Long , Robert C. Williamson, Fat-shattering and the learnability of real-valued functions, Proceedings of the seventh annual conference on Computational learning theory, p.299-310, July 12-15, 1994, New Brunswick, New Jersey, United States [doi>10.1145/180139.181158]
	5	Gyora M. Benedek , Alon Itai, Learnability with respect to fixed distributions, Theoretical Computer Science, v.86 n.2, p.377-389, Sept. 2, 1991 [doi>10.1016/0304-3975(91)90026-X]
	6	Anselm Blumer , A. Ehrenfeucht , David Haussler , Manfred K. Warmuth, Learnability and the Vapnik-Chervonenkis dimension, Journal of the ACM (JACM), v.36 n.4, p.929-965, Oct. 1989 [doi>10.1145/76359.76371]
	7	Andrzej Ehrenfeucht , David Haussler, A general lower bound on the number of examples needed for learning, Information and Computation, v.82 n.3, p.247-261, Sep. 1989 [doi>10.1016/0890-5401(89)90002-3]
	8	Leonid Gurvits , Pascal Koiran, Approximation and learning of convex superpositions, Proceedings of the Second European Conference on Computational Learning Theory, p.222-236, March 13-15, 1995
	9	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]
	10	David Haussler, Sphere packing numbers for subsets of the Boolean n-cube with bounded Vapnik-Chervonenkis dimension, Journal of Combinatorial Theory Series A, v.69 n.2, p.217-232, Feb. 1995 [doi>10.1016/0097-3165(95)90052-7]
	11	David Haussler , Michael Kearns, Equivalence of models for polynomial learnability, Information and Computation, v.95 n.2, p.129-161, Dec. 1991 [doi>10.1016/0890-5401(91)90042-Z]
	12	D. Haussler , N. Littlestone , M. K. Warmuth, Predicting {0, 1}-functions on randomly drawn points, Information and Computation, v.115 n.2, p.248-292, Dec. 1994 [doi>10.1006/inco.1994.1097]
	13	M.J. Kearns and R. E. Schapire. Efficient distributionfree learning of probabilistic concepts. In Prec. of the 31st Synzposium on the Foundations of Comp. Sci., pages 382-391. IEEE Computer Society Press, Los Alamitos, CA, 1990.
	14	Michael J. Kearns , Robert E. Schapire , Linda M. Sellie, Toward efficient agnostic learning, Proceedings of the fifth annual workshop on Computational learning theory, p.341-352, July 27-29, 1992, Pittsburgh, Pennsylvania, United States [doi>10.1145/130385.130424]
	15	D. Pollard. Convergence of Stochastic Processes. Springer, New York, 1984.
	16	Hans Ulrich Simon, Bounds on the number of examples needed for learning functions, Proceedings of the first European conference on Computational learning theory, p.83-94, October 1994, Royal Holloway, Univ. of London, United Kingdom

CITED BY 4

	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
	Shahar Mendelson, Learnability in Hilbert spaces with reproducing kernels, Journal of Complexity, v.18 n.1, p.152-170, March 2002
	Martin Anthony, Aspects of discrete mathematics and probability in the theory of machine learning, Discrete Applied Mathematics, v.156 n.6, p.883-902, March, 2008
	Arne Hole, Vapnik-chervonenkis generalization bounds for real valued neural networks, Neural Computation, v.8 n.6, p.1277-1299, August 15, 1996