On learning from noisy and incomplete examples

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On learning from noisy and incomplete examples

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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: 353 - 360

Year of Publication: 1995

ISBN:0-89791-723-5

Authors

Scott E. Decatur	Aiken Computation Laboratory, Harvard University, Cambridge, MA
Rosario Gennaro	Laboratory for Computer Science, Massachusetts Institute of Technology, Cambridge, MA

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): 18, Downloads (12 Months): 27, Citation Count: 7

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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	Javed Aslam and Scott Decatur. Improved noisetolerant learning and generalized statistical queries. Technical Report TR-17-94, Harvard University, July 1994.
	2	Shai Ben-David , Eli Dichterman, Learning with restricted focus of attention, Proceedings of the sixth annual conference on Computational learning theory, p.287-296, July 26-28, 1993, Santa Cruz, California, United States [doi>10.1145/168304.168353]
	3	Shai Ben-David , Eli Dichterman, Learnability with Restricted Focus of Attention guarantees Noise-Tolerance, Proceedings of the 4th International Workshop on Analogical and Inductive Inference: Algorithmic Learning Theory, p.248-259, October 10-15, 1994
	4	Avrim Blum , Mona Singh, Learning functions of k terms, Proceedings of the third annual workshop on Computational learning theory, p.144-153, August 06-08, 1990, Rochester, New York, United States
	5	Scott Evan Decatur, Statistical queries and faulty PAC oracles, Proceedings of the sixth annual conference on Computational learning theory, p.262-268, July 26-28, 1993, Santa Cruz, California, United States [doi>10.1145/168304.168346]
	6	Scott Decatur. Learning in hybrid noise environments using statistical queries. In Proceedings of the Fifth International Workshop on Art~ficzal Intelhgence and Statistics, pages 175-185, January 1995.
	7	Sally Goldman and Robert Sloan. Can PAC learning algorithms tolerate random attribute noise? Technical Report WUCS-92-25, Washington University, 1992.
	8	Michael Kearns, Efficient noise-tolerant learning from statistical queries, Proceedings of the twenty-fifth annual ACM symposium on Theory of computing, p.392-401, May 16-18, 1993, San Diego, California, United States [doi>10.1145/167088.167200]
	9	J. R. Quinlan, Induction of Decision Trees, Machine Learning, v.1 n.1, p.81-106 [doi>10.1023/A:1022643204877]
	10	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
	11	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
	12	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]

CITED BY 7

	Javed A. Aslam , Scott E. Decatur, Specification and simulation of statistical query algorithms for efficiency and noise tolerance, Proceedings of the eighth annual conference on Computational learning theory, p.437-446, July 05-08, 1995, Santa Cruz, California, United States
	Nader H. Bshouty , Jeffrey C. Jackson , Christino Tamon, Uniform-distribution attribute noise learnability, Proceedings of the twelfth annual conference on Computational learning theory, p.75-80, July 07-09, 1999, Santa Cruz, California, United States
	Andreas Birkendorf , Eli Dichterman , Jeffrey Jackson , Norbert Klasner , Hans Ulrich Simon, On restricted-focus-of-attention learnability of Boolean functions, Proceedings of the ninth annual conference on Computational learning theory, p.205-216, June 28-July 01, 1996, Desenzano del Garda, Italy
	Nader H. Bshouty , Nadav Eiron , Eyal Kushilevitz, PAC learning with nasty noise, Theoretical Computer Science, v.288 n.2, p.255-275, 16 October 2002
	Nader H. Bshouty , Jeffrey C. Jackson , Christino Tamon, Uniform-distribution attribute noise learnability, Information and Computation, v.187 n.2, p.277-290, 15 December 2003
	Andreas Birkendorf , Eli Dichterman , Jeffrey Jackson , Norbert Klasner , Hans Ulrich Simon, On Restricted-Focus-of-Attention Learnability of Boolean Functions, Machine Learning, v.30 n.1, p.89-123, Jan. 1998
	Loizos Michael, Learning from partial observations, Proceedings of the 20th international joint conference on Artifical intelligence, p.968-974, January 06-12, 2007, Hyderabad, India