V. N. Vapnik, Statistical Learning Theory, 1998.

G. Wahba, Spline Models for Observational Data, ser. CBMS-NSF Regional Conference Series in Applied Mathematics, 1990.

A. Barron, L. Birgé, and P. Massart, Risk bounds for model selection via penalization, Probability Theory and Related Fields, vol.113, issue.3, pp.301-413, 1999.
DOI : 10.1007/s004400050210

Y. Baraud, Model selection for regression on a random design, ESAIM: Probability and Statistics, vol.6, pp.127-146, 2002.
DOI : 10.1051/ps:2002007

P. Massart, Concentration Inequalities and Model Selection, ser. Lecture notes in mathematics, 2007.

P. Bartlett, O. Bousquet, and S. Mendelson, Local Rademacher complexities, The Annals of Statistics, vol.33, issue.4, pp.1497-1537, 2005.
DOI : 10.1214/009053605000000282

G. Blanchard, P. Massart, R. Vert, and L. Zwald, Kernel projection machine: a new tool for pattern recognition, Advances in Neural Information Processing System 17, pp.1649-1656, 2004.
URL : https://hal.archives-ouvertes.fr/hal-00373801

Y. Lin, Support Vector Machines and the Bayes rule in classification, Data Mining and Knowledge Discovery, vol.6, issue.3, pp.259-275, 2002.
DOI : 10.1023/A:1015469627679

T. Zhang, Statistical behavior and consistency of classification methods based on convex risk minimization, The Annals of Statistics, vol.32, issue.1, pp.56-85, 2004.
DOI : 10.1214/aos/1079120130

L. Györfi, M. Kohler, A. Krzy?, and H. Walk, A Distribution?Free Theory of Nonparametric Regression, ser, 2002.

P. L. Bartlett, The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network, IEEE Transactions on Information Theory, vol.44, issue.2, pp.525-536, 1998.
DOI : 10.1109/18.661502

D. Chen, Q. Wu, Y. Ying, and D. Zhou, Support vector machine soft margin classifiers: Error analysis, Journal of Machine Learning Research, vol.5, pp.1143-1175, 2004.

P. Massart and E. Nédélec, Risk bounds for statistical learning, The Annals of Statistics, vol.34, issue.5, pp.2326-2366, 2006.
DOI : 10.1214/009053606000000786

E. Mammen and A. B. Tsybakov, Asymptotical Minimax Recovery of Sets with Smooth Boundaries, The Annals of Statistics, vol.23, issue.2, pp.502-524, 1995.
DOI : 10.1214/aos/1176324533

A. Tsybakov, Optimal aggregation of classifiers in statistical learning, The Annals of Statistics, vol.32, issue.1, pp.135-166, 2004.
DOI : 10.1214/aos/1079120131
URL : https://hal.archives-ouvertes.fr/hal-00102142

A. Tsybakov and S. Van-de-geer, Square root penalty: Adaptation to the margin in classification and in edge estimation, The Annals of Statistics, vol.33, issue.3, pp.1203-1224, 2005.
DOI : 10.1214/009053604000001066
URL : https://hal.archives-ouvertes.fr/hal-00101837

S. V. De-geer and B. Tarigan, Classifiers of support vector machine type, with ? 1 penalty, Bernoulli, vol.12, pp.1045-1076, 2006.

J. Audibert, A Randomized Online Learning Algorithm for Better Variance Control, Proceedings of the 19th conference on computational learning theory (COLT 06), ser. Lecture notes in computer science, pp.392-407, 2006.
DOI : 10.1007/11776420_30

C. Scott and R. Nowak, Minimax-optimal classification with dyadic decision trees, IEEE Transactions on Information Theory, vol.52, issue.4, pp.1335-1353, 2006.
DOI : 10.1109/TIT.2006.871056

I. Steinwart and C. Scovel, Fast rates for support vector machines using Gaussian kernels, The Annals of Statistics, vol.35, issue.2, pp.575-607, 2007.
DOI : 10.1214/009053606000001226

E. D. Vito, L. Rosasco, A. Caponnetto, U. De-giovannini, and F. Odone, Learning from examples as an inverse problem, Journal of Machine Learning Research, vol.6, pp.883-904, 2005.

G. Blanchard, O. Bousquet, and P. Massart, Statistical performance of support vector machines Laboratoire de mathématiques, Tech. Rep, 2004.

T. Evgeniou, M. Pontil, and T. Poggio, Regularization networks and Support Vector Machines, " in Advances in Large Margin Classifiers, A, pp.171-203, 2000.

A. J. Smola and B. Schölkopf, On a Kernel-Based Method for Pattern Recognition, Regression, Approximation, and Operator Inversion, Algorithmica, vol.22, issue.1-2, pp.211-231, 1998.
DOI : 10.1007/PL00013831

. Baker, The Numerical Treatment of Integral Equations, Journal of Applied Mechanics, vol.46, issue.4, 1977.
DOI : 10.1115/1.3424708

B. Schölkopf, A. J. Smola, and K. Müller, Nonlinear Component Analysis as a Kernel Eigenvalue Problem, Neural Computation, vol.20, issue.5, pp.1299-1319, 1998.
DOI : 10.1007/BF02281970

V. Koltchinskii, Asymptotics of Spectral Projections of Some Random Matrices Approximating Integral Operators, Progress in Probability, pp.191-227, 1998.
DOI : 10.1007/978-3-0348-8829-5_11

J. Dauxois, A. Pousse, and Y. Romain, Asymptotic theory for the principal component analysis of a vector random function: Some applications to statistical inference, Journal of Multivariate Analysis, vol.12, issue.1, pp.136-154, 1982.
DOI : 10.1016/0047-259X(82)90088-4

L. Zwald and G. Blanchard, On the convergence of eigenspaces in kernel principal component analysis, Advances in neural information processing systems 18 (NIPS 2005), pp.1649-1656, 2006.
URL : https://hal.archives-ouvertes.fr/hal-00373803

G. Raetsch, 1999, benchmark repository used in several Boosting

N. Bissantz, T. Hohage, A. Munk, and F. Ruymgaart, Convergence Rates of General Regularization Methods for Statistical Inverse Problems and Applications, SIAM Journal on Numerical Analysis, vol.45, issue.6, pp.2610-2636, 2007.
DOI : 10.1137/060651884

V. Vapnik, Estimation of Dependences Based on Empirical Data, ser, 1982.

O. Bousquet, Concentration inequalities and empirical processes theory applied to the analysis of learning algorithms, 2002.

R. M. Dudley, Uniform Central Limit Theorems, ser. Cambridge Studies in advanced mathematics, 1999.

H. Zhu, C. Williams, R. Rohwer, and M. Morciniec, Gaussian regression and optimal finite dimensional linear models, Neural networks and machine learning, pp.167-184, 1998.

C. K. Williams and M. Seeger, The effect of the input density distribution on kernel-based classifiers, Proceedings of the 17th International Conference on Machine Learning, pp.1159-1166, 2000.