Uniform-distribution attribute noise learnability

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Uniform-distribution attribute noise learnability

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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: 75 - 80

Year of Publication: 1999

ISBN:1-58113-167-4

Authors

Nader H. Bshouty	Dept. Computer Science, Technion, Haifa 32000, Israel
Jeffrey C. Jackson	Math. & Comp. Science Dept., Duquesne University, Pittsburgh, PA
Christino Tamon	Dept. Math & Comp. Science, Clarkson University, Potsdam, NY

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): 4, Downloads (12 Months): 20, 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	A. Blum , C. Burch , J. Langford, On Learning Monotone Boolean Functions, Proceedings of the 39th Annual Symposium on Foundations of Computer Science, p.408, November 08-11, 1998
	2	Scott E. Decatur , Rosario Gennaro, On learning from noisy and incomplete examples, Proceedings of the eighth annual conference on Computational learning theory, p.353-360, July 05-08, 1995, Santa Cruz, California, United States [doi>10.1145/225298.225341]
	3	Luc Devroye, Lftszl6 Gy6rft, and Gfibor Lugosi. A Probabilistic Theory of Pattern Recognition. Springer- Verlag, 1996.
	4	Shaft Goldwasser and Silvio Micali. Probabilistic Encryption. In Journal of Computer and System Sciences, 28(2):270-299, 1984.
	5	Sally Goldman and Robert Sloan. Can PAC Learning Algorithms Tolerate Random Attribute Noise? In Algorithmica, 14(1):70-84, 1995.
	6	Michael Kearns , Ming Li, Learning in the presence of malicious errors, SIAM Journal on Computing, v.22 n.4, p.807-837, Aug. 1993 [doi>10.1137/0222052]
	7	Nathan Linial , Yishay Mansour , Noam Nisan, Constant depth circuits, Fourier transform, and learnability, Journal of the ACM (JACM), v.40 n.3, p.607-620, July 1993 [doi>10.1145/174130.174138]
	8	George Shackelford , Dennis Volper, Learning k-DNF with noise in the attributes, Proceedings of the first annual workshop on Computational learning theory, p.97-103, August 03-05, 1988, MIT, Cambridge, Massachusetts, United States
	9	Hans Ulrich Simon, General bounds on the number of examples needed for learning probabilistic concepts, Proceedings of the sixth annual conference on Computational learning theory, p.402-411, July 26-28, 1993, Santa Cruz, California, United States [doi>10.1145/168304.168385]

CITED BY 4

	Ryan O'Donnell, Hardness amplification within NP, Proceedings of the thiry-fourth annual ACM symposium on Theory of computing, May 19-21, 2002, Montreal, Quebec, Canada
	Nader H. Bshouty , Vitaly Feldman, On using extended statistical queries to avoid membership queries, The Journal of Machine Learning Research, 2, p.359-395, 3/1/2002
	Ryan O'Donnell, Hardness amplification within NP, Journal of Computer and System Sciences, v.69 n.1, p.68-94, August 2004
	Elchanan Mossel , Ryan O'Donnell, On the noise sensitivity of monotone functions, Random Structures & Algorithms, v.23 n.3, p.333-350, October 2003