Randomized approximate aggregating strategies and their applications to prediction and discrimination

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Randomized approximate aggregating strategies and their applications to prediction and discrimination

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

Santa Cruz, California, United States

Pages: 83 - 90

Year of Publication: 1995

ISBN:0-89791-723-5

Author

Kenji Yamanishi

NEC Research Institute, Inc., 4 Independence Way, Princeton, NJ

Sponsors

SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGART: ACM Special Interest Group on Artificial Intelligence
University of California : University of California

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 2, Downloads (12 Months): 16, Citation Count: 7

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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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	18	Kenji Yamanishi, Probably Almost Discriminative Learning, Machine Learning, v.18 n.1, p.23-50, Jan. 1995 [doi>10.1023/A:1022870506888]
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CITED BY 7

	Kenji Yamanishi, A randomized approximation of the MDL for stochastic models with hidden variables, Proceedings of the ninth annual conference on Computational learning theory, p.99-109, June 28-July 01, 1996, Desenzano del Garda, Italy
	Michael V. Vyugin , Vladimir V. V'yugin, On complexity of easy predictable sequences, Information and Computation, v.178 n.1, p.241-252, October 10, 2002
	V. G. Vovk, A game of prediction with expert advice, Proceedings of the eighth annual conference on Computational learning theory, p.51-60, July 05-08, 1995, Santa Cruz, California, United States
	V. V. V'yugin, Does snooping help?, Theoretical Computer Science, v.276 n.1-2, p.407-415, April 6, 2002
	V. V. V'yugin, Suboptimal measures of predictive complexity for absolute loss function, Information and Computation, v.175 n.2, p.146-157, June 15, 2002
	V. Vovk, Derandomizing Stochastic Prediction Strategies, Machine Learning, v.35 n.3, p.247-282, June 1999
	Michael V. Vyugin , Vladimir V. V'yugin, Predictive complexity and information, Journal of Computer and System Sciences, v.70 n.4, p.539-554, June 2005