J. C. Borda, Mémoire sur lesélectionslesélections au scrutin, Histoire de l'Académie Royale des Sciences (Académie Royale des Sciences, p.1781

R. Busa-fekete and B. Kégl, Fast boosting using adversarial bandits, International Conference on Machine Learning, pp.143-150, 2010.
URL : https://hal.archives-ouvertes.fr/in2p3-00614564

Z. Cao, T. Qin, T. Liu, M. Tsai, and H. Li, Learning to rank, Proceedings of the 24th international conference on Machine learning, ICML '07, pp.129-136, 2007.
DOI : 10.1145/1273496.1273513

N. Cesa-bianchi and G. Lugosi, Prediction, Learning, and Games, 2006.
DOI : 10.1017/CBO9780511546921

O. Chapelle and Y. Chang, Yahoo! Learning to Rank Challenge Overview, Yahoo Learning to Rank Challenge (JMLR W&CP), 2010.

S. Clémençon and N. Vayatis, Tree-Based Ranking Methods, IEEE Transactions on Information Theory, vol.55, issue.9, pp.4316-4336, 2009.
DOI : 10.1109/TIT.2009.2025558

C. Dwork, R. Kumar, M. Naor, and D. Sivakumar, Rank aggregation methods for the Web, Proceedings of the tenth international conference on World Wide Web , WWW '01, pp.613-622, 2001.
DOI : 10.1145/371920.372165

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

Y. Freund and R. E. Schapire, A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting, Journal of Computer and System Sciences, vol.55, issue.1, pp.119-139, 1997.
DOI : 10.1006/jcss.1997.1504

Y. Freund, R. 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.

B. Kégl and R. Busa-fekete, Boosting products of base classifiers, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pp.497-504, 2009.
DOI : 10.1145/1553374.1553439

P. Li, C. Burges, and Q. Wu, McRank: Learning to rank using multiple classification and gradient boosting, Advances in Neural Information Processing Systems, pp.897-904, 2007.

A. Niculescu-mizil and R. Caruana, Obtaining calibrated probabilities from boosting, Proceedings of the 21st International Conference on Uncertainty in Artificial Intelligence, pp.413-420, 2005.

R. E. Schapire and Y. Singer, Improved boosting algorithms using confidence-rated predictions, Proceedings of the eleventh annual conference on Computational learning theory , COLT' 98, pp.297-336, 1999.
DOI : 10.1145/279943.279960

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

H. Valizadegan, R. Jin, R. Zhang, and J. Mao, Learning to rank by optimizing NDCG measure, Advances in Neural Information Processing Systems 22, pp.1883-1891, 2009.