Minimax relative loss analysis for sequential prediction algorithms using parametric hypotheses

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Minimax relative loss analysis for sequential prediction algorithms using parametric hypotheses

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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: 32 - 43

Year of Publication: 1998

ISBN:1-58113-057-0

Author

Kenji Yamanishi

Theory NEC Laboratory, Real World Computing Partnership, C&C Media Research Laboratories, NEC Corporation, 1-1, 4-chome, Miyazaki, Miyamae-ku, Kawasaki, Kanagawa 216, Japan

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

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ACM New York, NY, USA

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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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