A. Tikhonov, Regularization of incorrectly posed problems, Soviet Mathematics Doklady, vol.4, pp.1624-1627, 1963.

B. Schölkopf and A. J. Smola, Learning with kernels, 2002.

J. Shawe-taylor and N. Cristanini, Kernel Methods for Pattern Analysis, 2004.
DOI : 10.1017/CBO9780511809682

L. Oneto, A. Ghio, S. Ridella, and D. Anguita, Support vector machines and strictly positive definite kernel: The regularization hyperparameter is more important than the kernel hyperparameters, 2015 International Joint Conference on Neural Networks (IJCNN), pp.1-4, 2015.
DOI : 10.1109/IJCNN.2015.7280413

S. Mendelson and J. Neeman, Regularization in kernel learning, The Annals of Statistics, vol.38, issue.1, pp.526-565, 2010.
DOI : 10.1214/09-AOS728

I. Steinwart, D. Hush, C. Scovel, and O. , Optimal rates for regularized least squares regression, COLT Proceedings, 2009.

I. Steinwart and C. Scovel, Fast rates to bayes for kernel machines, NIPS, 2005.

C. Saunders, A. Gammerman, and V. Vovk, Ridge regression learning algorithm in dual variables, " ser, Advances in Neural Information Processing Systems, 1998.

K. Bredies and D. A. Lorenz, Regularization with non-convex separable constraints, Inverse Problems, vol.25, issue.8, p.85011, 2009.
DOI : 10.1088/0266-5611/25/8/085011

V. V. Vasin, Relationship of several variational methods for the approximate solution of ill-posed problems, Mathematical Notes of the Academy of Sciences of the USSR, vol.6, issue.No. 4, pp.161-165, 1970.
DOI : 10.1007/BF01093105

R. Rifkin, Everything old is new again: A fresh look at historical approaches in machine learning, 2002.

I. Steinwart and C. Scovel, Fast Rates to Bayes for Kernel Machines, Advances in Neural Information Processing Systems, pp.1345-1352, 2005.

I. Steinwart and A. Christmann, Support vector machines, 2008.

F. Orabona, Simultaneous model selection and optimization through parameter-free stochastic learning, Advances in Neural Information Processing Systems, pp.1116-1124, 2014.

F. Dinuzzo and B. Schölkopf, The representer theorem for Hilbert spaces: a necessary and sufficient condition, " ser, Advances in Neural Information Processing Systems, 2012.

K. Bredies, D. Lorenz, and S. Reiterer, Minimization of Nonsmooth , Non-convex Functionals by Iterative Thresholding, Journal of Optimization Theory and Applications, 2014.

H. Widom, Asymptotic behavior of the eigenvalues of certain integral equations. II, Archive for Rational Mechanics and Analysis, vol.17, issue.3, pp.215-229, 1964.
DOI : 10.1007/BF00282438

O. Bousquet and A. Elisseeff, Stability and generalization, Journal of Machine Learning Research, vol.2, pp.499-526, 2002.

D. Mitrinovi´cmitrinovi´c, J. P?, and A. Fink, Classical and New Inequalities in Analysis, 1993.

I. W. Tsang, J. T. Kwok, and K. T. Lai, Core Vector Regression for very large regression problems, Proceedings of the 22nd international conference on Machine learning , ICML '05, 2005.
DOI : 10.1145/1102351.1102466