Learning with maximum-entropy distributions

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

Learning with maximum-entropy distributions

Full text

Pdf (2.04 MB)

Source

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

Nashville, Tennessee, United States

Pages: 201 - 210

Year of Publication: 1997

ISBN:0-89791-891-6

Authors

Yishay Mansour	Computer Science Dept., Tel-Aviv University
Mariano Schain	Computer Science Dept., Tel-Aviv University

Sponsors

AT&T Labs :
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGART: ACM Special Interest Group on Artificial Intelligence
Vanderbilt University : Vanderbilt University

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 1, Downloads (12 Months): 8, Citation Count: 0

Additional Information:

references index terms collaborative colleagues peer to peer

Tools and Actions:	Review this Article Save this Article to a Binder Display Formats: BibTeX EndNote ACM Ref

DOI Bookmark:	Use this link to bookmark this Article: http://doi.acm.org/10.1145/267460.267499 What is a DOI?

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.

	AK91	Dana Angluin , Michael Kharitonov, When won't membership queries help?, Proceedings of the twenty-third annual ACM symposium on Theory of computing, p.444-454, May 05-08, 1991, New Orleans, Louisiana, United States [doi>10.1145/103418.103420]
	BI91	Gyora M. Benedek , Alon Itai, Learnability with respect to fixed distributions, Theoretical Computer Science, v.86 n.2, p.377-389, Sept. 2, 1991 [doi>10.1016/0304-3975(91)90026-X]
	CT91	Thomas M. Cover , Joy A. Thomas, Elements of information theory, Wiley-Interscience, New York, NY, 1991
	HKLW91	David Haussler , Michael Kearns, Equivalence of models for polynomial learnability, Information and Computation, v.95 n.2, p.129-161, Dec. 1991 [doi>10.1016/0890-5401(91)90042-Z]
	HM91	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
	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]
	KM93	Daphne Koller , Nimrod Megiddo, Constructing small sample spaces satisfying given constraints, Proceedings of the twenty-fifth annual ACM symposium on Theory of computing, p.268-277, May 16-18, 1993, San Diego, California, United States [doi>10.1145/167088.167168]
	KN95	On the complexity of statistical reasoning, Proceedings of the 3rd Israel Symposium on the Theory of Computing Systems (ISTCS'95), p.209, January 04-06, 1995
	KV89	M. Kearns , L. G. Valiant, Crytographic limitations on learning Boolean formulae and finite automata, Proceedings of the twenty-first annual ACM symposium on Theory of computing, p.433-444, May 14-17, 1989, Seattle, Washington, United States [doi>10.1145/73007.73049]
	KLV94	Michael Kearns , Ming Li , Leslie Valiant, Learning Boolean formulas, Journal of the ACM (JACM), v.41 n.6, p.1298-1328, Nov. 1994 [doi>10.1145/195613.195656]
	Nat87	B. K. Natarajan, On learning Boolean functions, Proceedings of the nineteenth annual ACM conference on Theory of computing, p.296-304, January 1987, New York, New York, United States [doi>10.1145/28395.28427]
	Pap91	Athanasios Papoulis. Probability, Random Variables, and Stochastic Processes, Chapter 15. McGraw-Hill, third edition, 1991.
	PV88	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]
	SWG85	C.Ray Smith and Jr. W.T. Grandy, editors. Maximum-Entropy and Bayesian Methods in Inverse Problems. D.Reidel Publishing Company, 1985.
	Val84	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]