A randomized online learning algorithm for better variance control

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4005 LNAI, Issue , 2006, Pages 392-407

  Audibert, Jean Yves ^a  

^a UNIVERSITÉ PARIS EST   (France)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL COMPLEXITY; FUNCTIONS; ONLINE SYSTEMS; OPTIMIZATION; REGRESSION ANALYSIS; STATISTICAL METHODS;

EXPONENTIAL MOMENT CONDITION; SEQUENTIAL RANDOMIZED ALGORITHM; VARIANCE CONTROL;

LEARNING ALGORITHMS;

EID: 33746043351     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11776420_30     Document Type: Conference Paper

References (24)

1. P. Alquier. Iterative feature selection in least square regression estimation, 2005. Laboratoire de Probabilités et Modèles Aléatoires, Universités Paris 6 and Paris 7.
2. J.-Y. Audibert. Aggregated estimators and empirical complexity for least square regression. Ann. Inst. Henri Poincaré, Probab. Stat, 40(6):685-736, 2004.
3. J.-Y. Audibert. A better variance control for PAC-Bayesian classification. Preprint n.905, http://www.proba.jussieu.fr/mathdoc/preprints/ index.html, 2004. Laboratoire de Probabilités et Modèles Aléatoires, Universités Paris 6 and Paris 7.
4. J.-Y. Audibert. PAC-Bayesian statistical learning theory. PhD thesis, Laboratoire de Probabilités et Modèles Aléatoires, Universités Paris 6 and Paris 7, 2004.
5. A. Barron. Are bayes rules consistent in information? In T.M. Cover and B. Gopinath, editors, Open Problems in Communication and Computation, pages 85-91. Springer, 1987.
6. A. Barron and Y. Yang. Information-theoretic determination of minimax rates of convergence. Ann. Stat., 27(5): 1564-1599, 1999.
7. F. Bunea and A. Nobel. Sequential procedures for aggregating arbitrary estimators of a conditional mean, 2005. Technical report, Available from http://stat.fsu.edu/%7Eflori/ps/bnapril2005IEEE.pdf.
8. O. Catoni, Statistical Learning Theory and Stochastic Optimization: Ecole d'été de Probabilités de Saint-Flour XXXI - 2001. Lecture Notes in Mathematics. Springer Verlag.
9. O. Catoni. A mixture approach to universal model selection, preprint LMENS 97-30, Available from http://www.dma.ens.fr/edition/preprints/Index.97. html, 1997.
10. O. Catoni, Universal aggregation rules with exact bias bound. Preprint n.510, http://www.proba.jussieu.fr/mathdoc/preprints/index.html#1999, 1999.
11. O. Catoni. A PAC-Bayesian approach to adaptive classification. Preprint n.840, Laboratoire de Probabilités et Modèles Aléatoires, Universités Paris 6 and Paris 7, 2003.
12. N. Cesa-Bianchi, Y. Freund, D. Haussler, D.P. Helmbold, R.E. Schapire, and M.K. Warmuth. How to use expert advice. J. ACM, 44(3):427-485, 1997.
13. N. Cesa-Bianchi and G. Lugosi. On prediction of individual sequences, Ann. Stat., 27(6):1865-1895, 1999.
14. R.M. Dudley. Central limit theorems for empirical measures. Ann. Probab., 6:899-929, 1978.
15. D. Haussler, J. Kivinen, and M. K. Warmuth. Sequential prediction of individual sequences under general loss functions. IEEE Trans. on Information Theory, 44(5): 1906-1925, 1998.
16. A. Juditsky, P. Rigollet, and A.B. Tsybakov. Learning by mirror averaging, 2005. Available from arxiv website.
17. J. Kivinen and M. K. Warmuth. Averaging expert predictions. Lecture Notes in Computer Science, 1572:153-167, 1999.
18. Merhav and Feder. Universal prediction. IEEE Transactions on Information Theory, 44, 1998.
19. V. Vapnik. The nature of statistical learning theory. Springer-Verlag, second edition, 1995.
20. V.G. Vovk. Aggregating strategies. In COLT '90: Proceedings of the third annual workshop on Computational learning theory, pages 371-386, San Francisco, CA, USA, 1990. Morgan Kaufmann Publishers Inc.
21. V.G. Vovk. A game of prediction with expert advice. Journal of Computer and System Sciences, pages 153-173, 1998.
22. Y. Yang. Combining different procedures for adaptive regression. Journal of multivariate analysis, 74:135-161, 2000.
23. R. Yaroshinsky, R. El-Yaniv, and S.S. Seiden. How to better use expert advice. Mach. Learn., 55(3):271-309, 2004.
24. T. Zhang. Information theoretical upper and lower bounds for statistical estimation. IEEE Transaction on Information Theory, 2006. to appear.