Performance bounds for nonlinear time series prediction

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Performance bounds for nonlinear time series prediction

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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: 122 - 129

Year of Publication: 1997

ISBN:0-89791-891-6

Author

Ron Meir

Department of Electrical Engineering, Technion, Haifa 32000, Israel

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

Bibliometrics

Downloads (6 Weeks): 1, Downloads (12 Months): 17, Citation Count: 3

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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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	2	D. Bosque, Nonparametric Statistics for Stochastic Processes, Lecture Notes in Statistics 110, Springer Verlag 1996.
	3	Peter J Brockwell , Richard A Davis, Time series: theory and methods, Springer-Verlag New York, Inc., New York, NY, 1986
	4	Y.A. Davydov, "Mixing conditions for Markov chains", Thug of Prob. and its Applications, vol. 18, pp. 312- 328, 1973.
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	8	P Doukhan Mixing - Properties and Examples, Springer Verlag 1994.
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	10	G. Lugosi and K. Zeger, "Concept learning using complexity regularization", IEEE Trans. Inf. Theory, Vol. 42. No. 1 : 48-54, 1996.
	11	D. Modha and E. Masry, "Universal Prediction of Stationary Random Processes", submitted to IEEE Transactions on Information Theory, 1996.
	12	Vladimir Vapnik, Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics), Springer-Verlag New York, Inc., Secaucus, NJ, 1982
	13	V.N. Vapnik and A.Y. Chervonenkis, "On the uniform convergence of relative frequencies of events to their probabilities", Theory of Prob. and Applic., vol. I6 no. 2, pp. 264-280, 1971.
	14	A. W. van der Vaart and J. A. Wellner, Weak Convergence and Empirical Processes, Springer Verlag 1996.
	15	H. White, Estimation, Inference and Specification Analysis, Cambridge University Press, 1994.
	16	H. White and J.M. Wooldridge, "Some results on sieve estimation with dependent observations", in Nonparametric and Semi-parametric Methods in Econometrics and Statistics, W. A. Barnett, J. Powell and G. Tau then, EDs., Cambridge U. Press, 1991.
	17	B. Yu, "Rates of convergence for empirical processes of stationary mixing sequences", Ann. Prob., Vol. 22, No. 1, 94-116, 1994.
	18	A. Zeevi, R. Meir and V. Maiorov, "Error Bounds for Functional Approximation and Estimation Using Mixtures of Experts", submitted to IEEE Transactions on Information Theory, 1996.

CITED BY 3

	David Gamarnik, Extension of the PAC framework to finite and countable Markov chains, Proceedings of the twelfth annual conference on Computational learning theory, p.308-317, July 07-09, 1999, Santa Cruz, California, United States
	P. Kuusela , D. Ocone, Learning with side information: PAC learning bounds, Journal of Computer and System Sciences, v.68 n.3, p.521-545, May 2004
	Peter L. Bartlett , Stéphane Boucheron , Gábor Lugosi, Model Selection and Error Estimation, Machine Learning, v.48 n.1-3, p.85-113, 2002