An efficient extension to mixture techniques for prediction and decision trees

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An efficient extension to mixture techniques for prediction and decision trees

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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: 114 - 121

Year of Publication: 1997

ISBN:0-89791-891-6

Authors

Fernando Pereira	AT&T Labs, 600 Mountain Avenue, Murray Hill, NJ
Yoram Singer	AT&T Labs, 600 Mountain Avenue, Murray Hill, NJ

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

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Downloads (6 Weeks): 4, Downloads (12 Months): 18, Citation Count: 4

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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.

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CITED BY 4

	David A. McAllester, PAC-Bayesian Stochastic Model Selection, Machine Learning, v.51 n.1, p.5-21, April 2003
	David A. McAllester, PAC-Bayesian model averaging, Proceedings of the twelfth annual conference on Computational learning theory, p.164-170, July 07-09, 1999, Santa Cruz, California, United States
	Eiji Takimoto , Manfred K. Warmuth, Predicting nearly as well as the best pruning of a planar decision graph, Theoretical Computer Science, v.288 n.2, p.217-235, 16 October 2002
	Eiji Takimotoa , Akira Maruokaa , Volodya Vovk, Predicting nearly as well as the best pruning of a decision tree through dynamic programming scheme, Theoretical Computer Science, v.261 n.1, p.179-209, 06/17/2001