M. Belkin, P. Niyogi, and V. Sindhwani, Réalisation des expériences sur d'autres données Dérivation des chemins multi-dimensionels pour différents paramètres Utilisation des méthodes d'optimisation non-régulière et stochastique pour la recherche du modèle References I Manifold Regularization : A Geometric Framework for Learning from Labeled and Unlabeled Examples, Journal of Machine Learning Research, vol.7, pp.2399-2434, 2006.

L. Bottou and O. Bousquet, Learning Using Large Datasets, Mining Massive DataSets for Security, 2008.

O. Chapelle, Training a Support Vector Machine in the Primal, Neural Computation, vol.6, issue.5, pp.1155-1178, 2007.
DOI : 10.1198/106186005X25619

O. Chapelle and S. S. Keerthi, Efficient algorithms for ranking with SVMs, Information Retrieval, vol.6, issue.6, 2009.
DOI : 10.1007/s10791-009-9109-9

F. H. Clarke, Y. S. Ledyaev, R. J. Stern, and P. R. Wolenski, Nonsmooth analysis and control theory, 1998.
DOI : 10.1007/978-1-4614-1806-1_69
URL : https://hal.archives-ouvertes.fr/hal-00863298

I. References, T. Do, and . Artières, A Fast Method for Training Linear SVM in the Primal, ECML PKDD '08 : Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases -Part I, pp.272-287, 2008.

G. Gasso, K. Zapién, and S. Canu, Sparsity regularization path for semi-supervised SVM, Sixth International Conference on Machine Learning and Applications (ICMLA 2007)
DOI : 10.1109/ICMLA.2007.81

G. Gasso, K. Zapién, and S. Canu, Apprentissage semi supervisé via un SVM parcimonieux : calcul du chemin de régularisation

G. Gasso, K. Zapién, and S. Canu, Apprentissage semi supervisé via un SVM parcimonieux : calcul du chemin de régularisation

I. T. References, S. Hastie, R. Rosset, J. Tibshirani, and . Zhu, The Entire Regularization Path for the Support Vector Machine, Journal of Machine Learning Research, vol.5, pp.1391-1415, 2004.

T. Joachims, Training linear SVMs in linear time, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '06, pp.217-226, 2006.
DOI : 10.1145/1150402.1150429

G. S. Kimeldorf and G. Wahba, Some results on Tchebycheffian spline functions, Journal of Mathematical Analysis and Applications, vol.33, issue.1, pp.82-95, 1971.
DOI : 10.1016/0022-247X(71)90184-3

G. Loosli, Méthodes à noyaux pour la détection de contexte, 2007.

H. M. Markowitz, The Utility of Wealth, Journal of Political Economy, vol.60, issue.2, pp.151-158, 1952.
DOI : 10.1086/257177

I. M. References, B. Osborne, B. Presnell, and . Turlach, A New Approach to Variable Selection in Least Squares Problems, IMA journal of numerical analysis, vol.20, issue.3, pp.389-403, 1999.

J. Platt, Fast training of support vector machines using sequential minimal optimization, Advances in Kernel Methods : Support Vector Learning, pp.185-208, 1999.

S. Rosset and J. Zhu, Piecewise linear regularized solution paths, The Annals of Statistics, vol.35, issue.3, pp.1012-1030, 2007.
DOI : 10.1214/009053606000001370
URL : http://arxiv.org/abs/0708.2197

S. Shalev-shwartz, Y. Singer, and N. Srebro, Pegasos, Proceedings of the 24th international conference on Machine learning, ICML '07
DOI : 10.1145/1273496.1273598

V. K. References, T. Zapién, G. Gärtner, S. Gasso, and . Canu, Model Selection for Ranking SVM Using Regularization Path

K. Zapién, G. Gasso, T. Gärtner, and S. Canu, Model Selection for Ranking SVM Using Regularization Path, 2009.