Efficient noise-tolerant learning from statistical queries

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	1	Dana Angluin , Philip Laird, Learning From Noisy Examples, Machine Learning, v.2 n.4, p.343-370, April 1988 [doi>10.1023/A:1022873112823]
	2	Eric B. Baum , Yuh-Dauh Lyuu, The transition to perfect generalization in perceptrons, Neural Computation, v.3 n.3, p.386-401, Fall 1991
	3	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]
	4	A. Ehrenfeucht , David Haussler , Michael Kearns , Leslie Valiant, A general lower bound on the number of examples needed for learning, Proceedings of the first annual workshop on Computational learning theory, p.139-154, August 03-05, 1988, MIT, Cambridge, Massachusetts, United States
	5	Merrick L. Furst , Jeffrey C. Jackson , Sean W. Smith, Improved learning of AC0 functions, Proceedings of the fourth annual workshop on Computational learning theory, p.317-325, August 05-07, 1991, Santa Cruz, California, United States
	6	E. Gardner and B. Derrida. Three unfinished works on the optimal storage capacity of networks. 3. Phys. A: Math. Gen., 22:1983-1994, 1989.
	7	Thomas Hancock , Yishay Mansour, Learning monotone ku DNF formulas on product distributions, Proceedings of the fourth annual workshop on Computational learning theory, p.179-183, August 05-07, 1991, Santa Cruz, California, United States
	8	D. Haussler, Quantifying inductive bias: AI learning algorithms and Valiant's learning framework, Artificial Intelligence, v.36 n.2, p.177-221, September 1988 [doi>10.1016/0004-3702(88)90002-1]
	9	David Helmbold , Robert Sloan , Manfred K. Warmuth, Learning integer lattices, SIAM Journal on Computing, v.21 n.2, p.240-266, April 1992 [doi>10.1137/0221019]
	10	Michael J. Kearns, Computational Complexity of Machine Learning, MIT Press, Cambridge, MA, 1990
	11	Michael Kearns , Ming Li, Learning in the presence of malicious errors, Proceedings of the twentieth annual ACM symposium on Theory of computing, p.267-280, May 02-04, 1988, Chicago, Illinois, United States [doi>10.1145/62212.62238]
	12	M. Kearns , M. Li , L. Pitt , L. Valiant, On the learnability of Boolean formulae, Proceedings of the nineteenth annual ACM conference on Theory of computing, p.285-295, January 1987, New York, New York, United States [doi>10.1145/28395.28426]
	13	Michael Kearns , Leonard Pitt, A polynomial-time algorithm for learning k-variable pattern languages from examples, Proceedings of the second annual workshop on Computational learning theory, p.57-71, December 1989, Santa Cruz, California, United States
	14	Michael J. Kearns , Robert E. Schapire, Efficient distribution-free learning of probabilistic concepts, Journal of Computer and System Sciences, v.48 n.3, p.464-497, June 1994 [doi>10.1016/S0022-0000(05)80062-5]
	15	Philip D. Laird, Learning from good and bad data, Kluwer Academic Publishers, Norwell, MA, 1988
	16	Nathan Linial, Yishay Mansour, and Noam Nisan. Constant depth circuits, Fourier transform, and learnability. In 6!Oth Annual Symposium on Foundations of Computer Science, pages 574-579, October 1989.
	17	Marvin L. Minsky , Seymour A. Papert, Perceptrons: expanded edition, MIT Press, Cambridge, MA, 1988
	18	Leonard Pitt , Leslie G. Valiant, Computational limitations on learning from examples, Journal of the ACM (JACM), v.35 n.4, p.965-984, Oct. 1988 [doi>10.1145/48014.63140]
	19	Ronald L. Rivest, Learning Decision Lists, Machine Learning, v.2 n.3, p.229-246, November 1987 [doi>10.1023/A:1022607331053]
	20	Yasubumi Sakakibara. Algorithmic Learning of Formal Languages and Decision Trees. PhD thesis, Tokyo Institute of Technology, October 1991. Research Report IIAS-RR-91- 22E, International Institute for Advanced Study of Social Information Science, Fujitsu Laboratories, Ltd.
	21	Robert E. Schapire, Learning probabilistic read-once formulas on product distributions, Proceedings of the fourth annual workshop on Computational learning theory, p.184-198, August 05-07, 1991, Santa Cruz, California, United States
	22	Robert E. Schapire, The design and analysis of efficient learning algorithms, MIT Press, Cambridge, MA, 1992
	23	H.S. Seung, H. Sompolinsky, and N. Tishby. Statistical mechanics of learning from examples. Physical Review A, 45(8):6056-6091, April 1992.
	24	Robert Sloan, Types of noise in data for concept learning, Proceedings of the first annual workshop on Computational learning theory, p.91-96, August 03-05, 1988, MIT, Cambridge, Massachusetts, United States
	25	L. G. Valiant, A theory of the learnable, Communications of the ACM, v.27 n.11, p.1134-1142, Nov. 1984 [doi>10.1145/1968.1972]
	26	L. G. Valiant. Learning disjunctions of conjunctions. In Proceedings of the 9th International Joint Conference on Artificial Intelligence, pages 560-566, August 1985.
	27	V. N. Vapnik and A. Ya. Chervonenkis. On the uniform convergence of relative frequencies of events to their probabilities. Theory of Probability and its applications, XVI(2):264- 280, 1971.