Sample complexity of model-based search

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

Sample complexity of model-based search

Full text

Pdf (1.28 MB)

Source

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

Madison, Wisconsin, United States

Pages: 259 - 267

Year of Publication: 1998

ISBN:1-58113-057-0

Author

Christopher D. Rosin

The Scripps Research Institute and University of California, San Diego, CSE Dept., and The Scripps Research Institute, Mail Drop MB5, 10550 North Torrey Pines Road, La Jolla, CA

Sponsors

University of Wisconsin : University of Wisconsin
UC @ Santa Cruz : UC @ Santa Cruz
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): 0, Downloads (12 Months): 5, 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/10.1145/279943.279994 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.

	1	D. Aldous and U. Vazirani. "Go with the winners" algorithms. In S. Goldwasser, editor, Proceedings of the S5th Annual Symposium on Foundations of Computer Science. IEEE, 1994.
	2	S. Baluja and R. Caruana. Removing the genetics from the standard genetic algorithm. In A. Prieditis and S. Russell, editors, Proceedings of the Twelfth International Conference on Machine Learning. Morgan Kaufmann, 1995.
	3	Shumeet Baluja , Scott Davies, Fast probabilistic modeling for combinatorial optimization, Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence, p.469-476, July 1998, Madison, Wisconsin, United States
	4	George E P Box , Norman R Draper, Empirical model-building and response surface, John Wiley & Sons, Inc., New York, NY, 1986
	5	Justin A. Boyan , Andrew W. Moore, Learning evaluation functions for global optimization and Boolean satisfiability, Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence, p.3-10, July 1998, Madison, Wisconsin, United States
	6	D.A. Cohn, Z. Ghahramani, and M.I. Jordan. Active learning with statistical models. Journal of Artificial Intelligence Research, 4:129-145, 1996.
	7	Tassos Dimitriou , Russell Impagliazzo, Towards an analysis of local optimization algorithms, Proceedings of the twenty-eighth annual ACM symposium on Theory of computing, p.304-313, May 22-24, 1996, Philadelphia, Pennsylvania, United States [doi>10.1145/237814.237882]
	8	William E. Hart , Richard K. Belew, Optimization with genetic algorithm hybrids that use local searches, Adaptive individuals in evolving populations: models and algorithms, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1996
	9	David Haussler, Decision theoretic generalizations of the PAC model for neural net and other learning applications, Information and Computation, v.100 n.1, p.78-150, Sept. 1992 [doi>10.1016/0890-5401(92)90010-D]
	10	John H. Holland, Adaptation in natural and artificial systems, MIT Press, Cambridge, MA, 1992
	11	S.A. Kauffman. The Origins of Order : Selforganization and Selection in Evolution. Oxford University Press, 1993.
	12	Michael J. Kearns , Umesh V. Vazirani, An introduction to computational learning theory, MIT Press, Cambridge, MA, 1994
	13	Jonas Mockus. Bayesian approach to global optimization : theory and applications. Kluwer Academic, 1989.
	14	A.W. Moore and J. Schneider. Memory-based stochastic optimization. In D.S. Touretzky, M.C. Mozer, and M.E. Hasselmo, editors, Advances in Neural Information Processing Systems $, pages 1066-1072. MIT Press, 1996.
	15	Andrew W. Moore , Jeff G. Schneider , Justin A. Boyan , Mary S. Lee, Q2: Memory-Based Active Learning for Optimizing Noisy Continuous Functions, Proceedings of the Fifteenth International Conference on Machine Learning, p.386-394, July 24-27, 1998
	16	P. Niyogi. Free to choose: investigating the sample complexity of active learning of real valued functions. In A. Prieditis and S. Russell, editors, Proceedings of the Twelfth International Conference on Machine Learning. Morgan Kaufmann, 1995.
	17	J.G. Topliss. Quantitative structure-activity relationships of drugs. Academic Press, 1983.