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On-line evaluation and prediction using linear functions
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Source 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: 21 - 31  
Year of Publication: 1997
ISBN:0-89791-891-6
Author
Philip M. Long  ISCS Department, National University of Singapore, Singapore 119260, Republic of Singapore
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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I. Barland. Some ideas on learning with directional feedback. Master's thesis, UC Santa Cruz, June 1992.
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Donald A. Berry and Bert Fristedt. Bandit Problems. Chapman and Hall, New York, 1985.
 
7
A.W. Biermann and P.M. Long. The composition of messages in speech-graphics interactive systems. Proceedings ~{the 1996 !nternationaI SYmposium on Spoken Dialogue, 1996.
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N. Ccsa-Bianchi, P.M. Long, and M.K. Warmuth. Worsl-case quadratic loss bounds for prediction using linear {unctions and gradient descent. IEEE Transactions (m Neural Networks, 7(3):604-619, 1996.
 
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T. Covcr. Behavior of sequential predictors of binary sequences. In Proceedings of the 4th Prague Conference on Information Theory, Statistical Decision Functions and Random Processes, pages 263-272. Publishing House of the Czechoslovak Academy of Sciences, 1965.
 
11
 
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V. Faber and J. Mycielski. Applications of learning theorems. Fundamenta lnformaticae, 15(2): 145-167, 1991.
 
13
 
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D.P. Helmbold, N. Littlestone, and P.M. Long. Apple tasting and nearly one-sided learning. Proceedings of the 33rd Annual Symposium on the Foundations of Computer Science, 1992.
 
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S. Kaczmarz. Angenaherte Aufl6sung yon systemen linearer gleichungen. Bull. Acad. Polon. Sci. Lett. A, 35:355-357, 1937.
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N. Merhav and M. Feder. Universal schemes for sequential decision from individual data sequences. IEEE Trans. Inform. Theory, 39(4):1280-1291, 1993.
 
26
J. Mycielski. A learning algorithm for linear operators. Proceedings of the American Mathematical Societ),, 103(2):547-550, 1988.
 
27
H.L. Royden. RealAnalysis. Macmillan, 1963.
 
28
B. Widrow and M.E. Hoff. Adaptive switching circuits. 1960 IRE WESCON Cony. Record, pages 96- 104, 1960.