PAC learning axis-aligned rectangles with respect to product distributions from multiple-instance examples

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PAC learning axis-aligned rectangles with respect to product distributions from multiple-instance examples

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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: 228 - 234

Year of Publication: 1996

ISBN:0-89791-811-8

Authors

Philip M. Long	ISCS Department, National University of Singapore, Singapore 119260, Republic of Singapore
Lei Tan	Department of Computer Science, Duke University, P.O. Box 90129, Durham, North Carolina

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): 17, Downloads (12 Months): 27, Citation Count: 5

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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	Anselm Blumer , A. Ehrenfeucht , David Haussler , Manfred K. Warmuth, Learnability and the Vapnik-Chervonenkis dimension, Journal of the ACM (JACM), v.36 n.4, p.929-965, Oct. 1989 [doi>10.1145/76359.76371]
	2	Thomas G. Dietterich , Richard H. Lathrop , Tomás Lozano-Pérez, Solving the multiple instance problem with axis-parallel rectangles, Artificial Intelligence, v.89 n.1-2, p.31-71, Jan. 1997 [doi>10.1016/S0004-3702(96)00034-3]
	3	Michael J. Kearns , Robert E. Schapire, Efficient distribution-free learning of probabilistic concepts, Journal of Computer and System Sciences, v.48 n.3, p.464-497, June 1994 [doi>10.1016/S0022-0000(05)80062-5]
	4	Philip M. Long , Manfred K. Warmuth, Composite geometric concepts and polynomial predictability, Information and Computation, v.113 n.2, p.230-252, Sept. 1994 [doi>10.1006/inco.1994.1071]
	5	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]
	6	D. Pollard. Convergence of Stochastic Processes. Springer Verlag, 1984.
	7	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]
	8	Kenji Yamanishi, A Learning Criterion for Stochastic Rules, Machine Learning, v.9 n.2-3, p.165-203, July 1992 [doi>10.1007/BF00992676]

CITED BY 5

	Peter Auer , Philip M. Long , Aravind Srinivasan, Approximating hyper-rectangles: learning and pseudo-random sets, Proceedings of the twenty-ninth annual ACM symposium on Theory of computing, p.314-323, May 04-06, 1997, El Paso, Texas, United States
	Stuart Andrews , Thomas Hofmann , Ioannis Tsochantaridis, Multiple instance learning with generalized support vector machines, Eighteenth national conference on Artificial intelligence, p.943-944, July 28-August 01, 2002, Edmonton, Alberta, Canada
	Sally A. Goldman , Stephen S. Kwek , Stephen D. Scott, Agnostic learning of geometric patterns (extended abstract), Proceedings of the tenth annual conference on Computational learning theory, p.325-333, July 06-09, 1997, Nashville, Tennessee, United States
	Philip M. Long , Lei Tan, PAC Learning Axis-aligned Rectangles with Respect toProduct Distributions from Multiple-Instance Examples, Machine Learning, v.30 n.1, p.7-21, Jan. 1998
	Avrim Blum , Adam Kalai, A Note on Learning from Multiple-Instance Examples, Machine Learning, v.30 n.1, p.23-29, Jan. 1998