R. Herbrich, T. Graepel, P. Bollmann-sdorra, and K. Obermayer, Learning a preference relation for information retrieval, Proceedings of the AAAI Workshop Text Categorization and Machine Learning, 1998.

W. Cohen, R. Schapire, and Y. Singer, Learning to order things, " in NIPS '97: Proceedings of the 1997 conference on Advances in neural information processing systems 10, pp.451-457, 1998.

M. Desjardins, E. Eaton, and K. Wagstaff, Learning user preferences for sets of objects, Proceedings of the 23rd international conference on Machine learning , ICML '06, pp.273-280, 2006.
DOI : 10.1145/1143844.1143879

R. Herbrich, T. Graepel, and K. Obermayer, Advances in Large Margin Classifiers, ch. Large margin rank boundaries for ordinal regression, pp.115-132, 2000.

K. Crammer and Y. Singer, Pranking with ranking, Proceedings of the conference on Neural Information Processing Systems (NIPS), 2001.

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.

S. Agarwal, T. Graepel, R. Herbrich, S. Har-peled, and D. Roth, Generalization bounds for the area under the ROC curve, Journal of Machine Learning Research, vol.6, pp.393-425, 2005.

S. Clémençon, G. Lugosi, and N. Vayatis, Ranking and scoring using empirical risk minimization, Proceedings of COLT 2005, ser. Lecture Notes in Computer Science, pp.1-15, 2005.

T. Hastie and R. Tibshirani, Generalized Additive Models, 1990.

D. M. Green and J. Swets, Signal detection theory and psychophysics, 1966.

H. Van-trees, Detection, Estimation, and Modulation Theory, Part I, 1968.
DOI : 10.1002/0471221082

J. Egan, Signal Detection Theory and ROC Analysis, 1975.

J. Hanley and J. Mcneil, The meaning and use of the area under a ROC curve, Radiology, issue.143, pp.29-36, 1982.

C. Cortes and M. Mohri, Auc optimization vs. error rate minimization, Advances in Neural Information Processing Systems 16, 2004.

A. Rakotomamonjy, Optimizing area under roc curve with svms, Proceedings of the First Workshop on ROC Analysis in AI, 2004.

L. Yan, R. Dodier, M. Mozer, and R. Wolniewicz, Optimizing classifier performance via an approximation to the wilcoxon-mann-whitney statistic, Proceedings of the Twentieth International Conference on Machine Learning, pp.848-855, 2003.

S. Clémençon, G. Lugosi, and N. Vayatis, Ranking and empirical risk minimization of U-statistics, The Annals of Statistics

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

C. Ferri, P. Flach, and J. Hernández-orallo, Learning decision trees using the area under the roc curve, ICML '02: Proceedings of the Nineteenth International Conference on Machine Learning, pp.139-146, 2002.

F. Provost and P. Domingos, Tree induction for probability-based ranking, Machine Learning, pp.199-215, 2003.

F. Xia, W. Zhang, and J. Wang, An effective tree-based algorithm for ordinal regression, IEEE Intelligent Informatics Bulletin, vol.7, issue.1, pp.22-26, 2006.

S. Clémençon and N. Vayatis, Ranking the best instances, Journal of Machine Learning Research, vol.8, pp.2671-2699, 2007.

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

L. Györfi, M. Köhler, A. Krzyzak, and H. Walk, A Distribution-Free Theory of Nonparametric Regression, 2002.
DOI : 10.1007/b97848

R. Devore and G. Lorentz, Constructive Approximation, 1993.

L. Breiman, Bagging predictors, Machine Learning, pp.123-140, 1996.
DOI : 10.1007/BF00058655

J. Friedman, T. Hastie, and R. Tibshirani, Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors), The Annals of Statistics, vol.28, issue.2, pp.337-407, 2000.
DOI : 10.1214/aos/1016218223

D. Hsieh and B. Turnbull, Nonparametric and semiparametric estimation of the receiver operating characteristic curve, The Annals of Statistics, vol.24, issue.1, pp.25-40, 1996.
DOI : 10.1214/aos/1033066197