Free Access
Issue
ESAIM: PS
Volume 14, 2010
Page(s) 315 - 337
DOI https://doi.org/10.1051/ps:2008036
Published online 29 October 2010
  1. P.L. Bartlett and S. Mendelson, Empirical minimization. Probab. Theory Relat. Fields 135 (2006) 311–334. [CrossRef] [Google Scholar]
  2. P.L. Bartlett and M.H. Wegkamp, Classification with a reject option using a hinge loss. J. Machine Learn. Res. 9 (2008) 1823–1840. [Google Scholar]
  3. P.L. Bartlett, O. Bousquet and S. Mendelson, Local Rademacher Complexities. Ann. Statist. 33 (2005) 1497–1537. [CrossRef] [MathSciNet] [Google Scholar]
  4. P.L. Bartlett, M.I. Jordan and J.D. McAuliffe, Convexity, classification, and risk bounds. J. Am. Statist. Assoc. 101 (2006) 138–156. [Google Scholar]
  5. G. Blanchard, G. Lugosi and N. Vayatis, On the rate of convergence of regularized boosting classifiers. J. Mach. Learn. Res. 4 (2003) 861–894. [Google Scholar]
  6. S. Boucheron, G. Lugosi and P. Massart, Concentration inequalities using the entropy method. Ann. Probab. 31 (2003) 1583–1614. [CrossRef] [MathSciNet] [Google Scholar]
  7. O. Bousquet, Concentration Inequalities and Empirical Processes Theory Applied to the Analysis of Learning Algorithms. Ph.D. thesis, École Polytechnique, 2002. [Google Scholar]
  8. R.M. Dudley, Uniform Central Limit Theorems, Cambridge University Press (1999). [Google Scholar]
  9. D. Haussler, Sphere Packing Numbers for Subsets of the Boolean n-cube with Bounded Vapnik-Chervonenkis Dimension. J. Combin. Theory Ser. A 69 (1995) 217–232. [CrossRef] [MathSciNet] [Google Scholar]
  10. T. Klein, Une inégalité de concentration gauche pour les processus empiriques. C. R. Math. Acad. Sci. Paris 334 (2002) 501–504. [Google Scholar]
  11. V. Koltchinskii, Local Rademacher Complexities and Oracle Inequalities in Risk Minimization. Ann. Statist. 34 (2006). [Google Scholar]
  12. V. Koltchinskii and D. Panchenko, Rademacher processes and bounding the risk of function learning. High Dimensional Probability, Vol. II (2000) 443–459. [Google Scholar]
  13. M. Ledoux, The Concentration of Measure Phenomenon, volume 89 of Mathematical Surveys and Monographs. American Mathematical Society (2001). [Google Scholar]
  14. W.S. Lee, P.L. Bartlett and R.C. Williamson, The Importance of Convexity in Learning with Squared Loss. IEEE Trans. Informa. Theory 44 (1998) 1974–1980. [Google Scholar]
  15. G. Lugosi and N. Vayatis, On the Bayes-risk consistency of regularized boosting methods (with discussion), Ann. Statist. 32 (2004) 30–55. [Google Scholar]
  16. G. Lugosi and M. Wegkamp, Complexity regularization via localized random penalties. Ann. Statist. 32 (2004) 1679–1697. [CrossRef] [MathSciNet] [Google Scholar]
  17. P. Massart, The constants in Talagrand's concentration inequality for empirical processes. Ann. Probab. 28 (2000) 863–884. [CrossRef] [MathSciNet] [Google Scholar]
  18. P. Massart, Some applications of concentration inequalities to statistics. Ann. Fac. Sci. Toulouse Math. IX (2000) 245–303. [Google Scholar]
  19. P. Massart and E. Nédélec, Risk bounds for statistical learning. Ann. Statist. 34 (2006) 2326–2366. [Google Scholar]
  20. S. Mendelson, Improving the sample complexity using global data. IEEE Trans. Inform. Theory 48 (2002) 1977–1991. [CrossRef] [MathSciNet] [Google Scholar]
  21. S. Mendelson, A few notes on Statistical Learning Theory. In Proc. of the Machine Learning Summer School, Canberra 2002, S. Mendelson and A. J. Smola (Eds.), LNCS 2600. Springer (2003). [Google Scholar]
  22. E. Rio, Inégalités de concentration pour les processus empiriques de classes de parties. Probab. Theory Relat. Fields 119 (2001) 163–175. [Google Scholar]
  23. M. Rudelson and R. Vershynin, Combinatorics of random processes and sections of convex bodies. Ann. Math. 164 (2006) 603–648. [CrossRef] [Google Scholar]
  24. M. Talagrand, Sharper Bounds for Gaussian and Empirical Processes. Ann. Probab. 22 (1994) 20–76. [Google Scholar]
  25. M. Talagrand, New concentration inequalities in product spaces. Inventiones Mathematicae 126 (1996) 505–563. [Google Scholar]
  26. B. Tarigan and S.A. Van de Geer, Adaptivity of support vector machines with Formula penalty. Technical Report MI 2004-14, University of Leiden (2004). [Google Scholar]
  27. A. Tsybakov, Optimal aggregation of classifiers in statistical learning. Ann. Statist. 32 (2004) 135–166. [Google Scholar]
  28. S.A. Van de Geer, A new approach to least squares estimation, with applications. Ann. Statist. 15 (1987) 587–602. [CrossRef] [MathSciNet] [Google Scholar]
  29. S.A. Van de Geer, Empirical Processes in M-Estimation, Cambridge University Press (2000). [Google Scholar]
  30. A. van der Vaart and J. Wellner, Weak Convergence and Empirical Processes. Springer (1996). [Google Scholar]
  31. V.N. Vapnik and A.Y. Chervonenkis, On the uniform convergence of relative frequencies of events to their probabilities. Theory Probab. Appl. 16 (1971) 264–280. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.