S. Clémençon and N. Vayatis, Tree-structured ranking rules and approximation of the optimal ROC curve, ALT '08: Proceedings of the 2008 conference on Algorithmic Learning Theory, 2008.

Y. Freund, R. D. Iyer, R. E. Schapire, and Y. Singer, An efficient boosting algorithm for combining preferences, Journal of Machine Learning Research, vol.4, pp.933-969, 2003.

J. Friedman, Local learning based on recursive covering, p.94305, 1996.

P. Flach and E. T. , A Simple Lexicographic Ranker and Probability Estimator, Proceedings of the 18th European Conference on Machine Learning. Joost N. Kok, Jacek Koronacki, pp.575-582, 2007.
DOI : 10.1007/978-3-540-74958-5_55

F. Provost and P. Domingos, Tree induction for probability-based ranking, Machine Learning, vol.52, issue.3, pp.199-215, 2003.
DOI : 10.1023/A:1024099825458

L. Breiman, J. Friedman, R. Olshen, and C. Stone, Classification and Regression Trees, 1984.

S. Clémençon and N. Vayatis, Tree-based ranking rules, 2008.

S. Clémençon, G. Lugosi, and N. Vayatis, Ranking and Empirical Minimization of U -statistics, The Annals of Statistics, vol.36, issue.2, pp.844-874, 2008.
DOI : 10.1214/009052607000000910

S. Clémençon and N. Vayatis, On partitioning rules for bipartite ranking, AISTATS '09: Proceedings of the 2009 conference on Artificial Intelligence and Statistics, 2009.

S. Mallat, A Wavelet Tour of Signal Processing, 1990.

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

S. Clémençon and N. Vayatis, Overlaying classifiers: a practical approach for optimal ranking, NIPS '08: Proceedings of the 2008 conference on Advances in neural information processing systems, 2008.

A. Nobel, Analysis of a complexity-based pruning scheme for classification trees, IEEE Transactions on Information Theory, vol.48, issue.8, pp.2362-2368, 2002.
DOI : 10.1109/TIT.2002.800482

B. Ripley, Pattern Recognition and Neural Networks, 1996.
DOI : 10.1017/CBO9780511812651

P. Massart, Concentration inequalities and model selection, Lecture Notes in Mathematics, 2006.

S. Boucheron, O. Bousquet, and G. Lugosi, Theory of Classification: a Survey of Some Recent Advances, ESAIM: Probability and Statistics, vol.9, pp.323-375, 2005.
DOI : 10.1051/ps:2005018

URL : https://hal.archives-ouvertes.fr/hal-00017923

S. Clémençon, G. Lugosi, and N. Vayatis, Ranking and Scoring Using Empirical Risk Minimization, Proceedings of COLT 2005, pp.1-15, 2005.
DOI : 10.1007/11503415_1

J. Friedman, Greedy Function Approximation: a Gradient Boosting Machine, IMS Reitz Lecture Annals of Statistics, vol.6, pp.393-425, 1999.

R. Serfling, Approximation Theorems of Mathematical Statistics., Biometrics, vol.37, issue.4, 1980.
DOI : 10.2307/2530199

G. Lugosi and K. Zeger, Concept learning using complexity regularization, IEEE Transactions on Information Theory, vol.42, issue.1, pp.48-54, 1996.
DOI : 10.1109/18.481777

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.91.4847

L. Devroye, L. Györfi, and G. Lugosi, A Probabilistic Theory of Pattern Recognition, 1996.
DOI : 10.1007/978-1-4612-0711-5