On restricted-focus-of-attention learnability of Boolean functions

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On restricted-focus-of-attention learnability of Boolean functions

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Annual Workshop on Computational Learning Theory archive
Proceedings of the ninth annual conference on Computational learning theory table of contents

Desenzano del Garda, Italy

Pages: 205 - 216

Year of Publication: 1996

ISBN:0-89791-811-8

Authors

Andreas Birkendorf	Universität Dortmund, Fachbereich Informatik, D-44221 Dortmund, Germany
Eli Dichterman	Computer Science Department, Technion, Haifa 32000, Israel
Jeffrey Jackson	Math and Computer Science Department, Duquesne University, 600 Forbes Avenue, Pittsburgh, PA
Norbert Klasner	Universität Dortmund, Fachbereich Informatik, D-44221 Dortmund, Germany
Hans Ulrich Simon	Universität Dortmund, Fachbereich Informatik, D-44221 Dortmund, Germany

Sponsors

Univ degli Studi de Milano : Universite degli Studi de Milano
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): 14, Downloads (12 Months): 24, Citation Count: 3

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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.

	BDD93	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]
	BDD94	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
	Bru90	Jehoshua Bruck. Harmonic analysis of polynomial threshold functions. SIAM Journal of Discrete Mathematics, 3(2): 168-177, May 1990.
	DG95	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]
	EHKV89	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]
	Fre90	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
	Fre93	Yoav Freund, Data filtering and distribution modeling algorithms for machine learning, University of California at Santa Cruz, Santa Cruz, CA, 1993
	Jac94	Jeffrey C. Jackson. An efficient membershipquery algorithm for learning DNF with respect to the uniform distribution. In Proceedings of the 35th Annual IEEE Symposium on Foundations of Computer Science, pages 42-53, 1994.
	Jac95	Jeffrey Charles Jackson, The harmonic sieve: a novel application of Fourier analysis to machine learning theory and practice, Carnegie Mellon University, Pittsburgh, PA, 1996
	Kea93	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]
	KS90	Michael J. Kearns and Robert E. Schapire. Efficient distribution-free learning of probabilistic concepts. In Proceedings of the 31st Annual IEEE Symposium on Foundations of Computer Science, pages 382-391, 1990.
	Riv87	Ronald L. Rivest, Learning Decision Lists, Machine Learning, v.2 n.3, p.229-246, November 1987 [doi>10.1023/A:1022607331053]

CITED BY 3

	Andreas Birkendorf , Norbert Klasner , Christian Kuhlmann , Hans U. Simon, Structural results about exact learning with unspecified attribute values, Proceedings of the eleventh annual conference on Computational learning theory, p.144-153, July 24-26, 1998, Madison, Wisconsin, United States
	Sally A. Goldman , Stephen S. Kwek , Stephen D. Scott, Learning from examples with unspecified attribute values (extended abstract), Proceedings of the tenth annual conference on Computational learning theory, p.231-242, July 06-09, 1997, Nashville, Tennessee, United States
	Stephen S. Kwek, An apprentice learning model (extended abstract), Proceedings of the twelfth annual conference on Computational learning theory, p.63-74, July 07-09, 1999, Santa Cruz, California, United States