Efficient learning of monotone concepts via quadratic optimization

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Efficient learning of monotone concepts via quadratic optimization

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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: 134 - 143

Year of Publication: 1998

ISBN:1-58113-057-0

Author

David Gamarnik

Mathematical Sciences Department, IBM, T.J. Watson Research Center, P.O. Box 218, Yorktown Heights, NY

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

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Downloads (6 Weeks): 4, Downloads (12 Months): 12, Citation Count: 1

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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	Dimitris Bertsimas , David Gamarnik , John N. Tsitsiklis, Estimation of time-varying parameters in statistical models: an optimization approach, Proceedings of the tenth annual conference on Computational learning theory, p.314-324, July 06-09, 1997, Nashville, Tennessee, United States [doi>10.1145/267460.267519]
	2	James M. Hutchinson , Andrew Lo , Tomaso Poggio, A Nonparametric Approach to Pricing and Hedging Derivative Securities Via Learning Networks, Massachusetts Institute of Technology, Cambridge, MA, 1994
	3	M. Kearns and R. Schapire. Efficient distributionfree learning of probabilistic concepts. 31st Annual Symposium on Foundations of Computer Science, 382-391, 1990.
	4	Pollard. Convergence of Stochastic Processes. Springer-Verlag, 1984.
	5	Vladimir N. Vapnik, The nature of statistical learning theory, Springer-Verlag New York, Inc., New York, NY, 1995
	6	M. Bazaara, H. Sherali, C. Shetti. Nonlinear Programming; Theory and Algorithms. Wiley, New York, 1993.
	7	David Haussler, DECISION THEORETIC GENERALIZATIONS OF THE PAC MODEL FORNEURAL NET AND OTHER LEARNING APPLICATIONS, University of California at Santa Cruz, Santa Cruz, CA, 1990