Proper learning algorithm for functions of k terms under smooth distributions

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Proper learning algorithm for functions of k terms under smooth distributions

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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: 206 - 213

Year of Publication: 1995

ISBN:0-89791-723-5

Authors

Yoshifumi Sakai	Graduate School of Information Sciences, Tohoku University, Sendai 980-77, Japan
Eiji Takimoto	Graduate School of Information Sciences, Tohoku University, Sendai 980-77, Japan
Akira Maruoka	Graduate School of Information Sciences, Tohoku University, Sendai 980-77, Japan

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

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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	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
	2	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]
	3	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]
	4	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]