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Analysis of two gradient-based algorithms for on-line regression
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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: 163 - 170  
Year of Publication: 1997
ISBN:0-89791-891-6
Author
Nicolò Cesa-Bianchi  DSI, University of Milan, Via Comelico 39, 20135 Milano, Italy
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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N. Cesa-Bianchi, PM. Long, and M.K. Warmuth. Worst-case quadratic loss bounds for prediction using linear functions and gradient descent. IEEE Transactions on Neural Networks, 7(3):604-619, 1996.
 
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M. Feder, N. Merhav, and M. Gutman. Universal prediction of individual sequences. IEEE Trans. on Information Theory, 38:1258-1270,1992.
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D.P. Helmbold, J. Kivinen, and M.K. Warmuth. Worstcase loss bounds for sigmoided neurons. In Advances in Neural Information Processing Systems 8. MIT Press, 1996.
 
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W. Hoeffding. Probability inequalities for sums of bounded random variables. Journal of the American Statistical Association, 58: 13-30, 1963.
 
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D. Pollard. Convergence of Stochastic Processes. Springer Verlag, 1984.
 
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K. Yamanishi. A decision-theoretic extension of stochastic complexity and its application to learning. Submitted for publication, 1995.


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