Learning Markov chains with variable memory length from noisy output

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Learning Markov chains with variable memory length from noisy output

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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: 298 - 308

Year of Publication: 1997

ISBN:0-89791-891-6

Authors

Dana Angluin	Department of Computer Science, Yale University, P.O Box 208285, New Haven CT
Miklós Csűrös	Department of Computer Science, Yale University, P.O Box 208285, New Haven CT

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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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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