M. Dettling and P. Bühlmann, Boosting for tumor classification with gene expression data, Bioinformatics, vol.19, issue.9, pp.1061-1069, 2003.
DOI : 10.1093/bioinformatics/btf867

H. Drucker, R. E. Schapire, and P. Simard, BOOSTING PERFORMANCE IN NEURAL NETWORKS, International Journal of Pattern Recognition and Artificial Intelligence, vol.07, issue.04, pp.705-719, 1993.
DOI : 10.1142/S0218001493000352

W. Fan, S. J. Stolfo, J. Zhang, and P. K. Chan, Adacost: Misclassification cost-sensitive Boosting

Y. Freund and R. E. Schapire, Experiments with a new Boosting algorithm, pp.148-156, 1996.

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

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

K. Grigoris and S. T. John, Optimizing classifers for imbalanced training sets, Neural Information Processing Systems, pp.253-259, 1998.

C. Huang, Z. H. Ai, B. Wu, and S. Lao, Boosting nested cascade detector for multi-view face detection, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., pp.415-418, 2004.
DOI : 10.1109/ICPR.2004.1334239

M. Kearns and L. G. Valiant, Cryptographic limitations on learning Boolean formulae and finite automata, Journal of the ACM, vol.41, issue.1, pp.67-95, 1994.
DOI : 10.1145/174644.174647

S. Z. Li, L. Zhu, Q. Z. Zhang, A. Blake, J. H. Zhang et al., Statistical Learning of Multi-view Face Detection, pp.67-81, 2002.
DOI : 10.1007/3-540-47979-1_5

Y. Ma and X. Q. Ding, Robust real-time face detection based on cost-sensitive AdaBoost method, pp.465-478, 2003.

R. Meir and G. Rätsch, An Introduction to Boosting and Leveraging, Advanced Lectures on Maching Learning, pp.118-183, 2003.
DOI : 10.1007/3-540-36434-X_4

T. Onoda, G. Rätsch, and K. R. Müller, A non-intrusive monitoring system for household electric appliances with inverters, 2000.

N. Oza and S. Russell, Experimental comparisions of online and batch versions of Bagging and Boosting, pp.359-364, 2001.

G. Rätsch, M. K. Warmuth, S. Mika, T. Onoda, S. Lemm et al., Barrier Boosting, pp.170-179, 2000.

R. E. Schapire, The strength of weak learnability, Machine Learning, pp.197-227, 1990.

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

H. Schwenk and Y. Bengio, Boosting Neural Networks, Neural Computation, vol.5, issue.2, pp.1869-1887, 2000.
DOI : 10.1162/neco.1992.4.1.1

URL : https://hal.archives-ouvertes.fr/hal-01434666

K. M. Ting and Z. Zheng, Boosting trees for cost-sensitive classifications
DOI : 10.1007/BFb0026689

P. Viola and M. Jones, Fast and robust classification using asymmetric Adaboost and a detector cascade, Neural Information Processing Systems, pp.1311-1318, 2001.

P. Viola and M. Jones, Rapid object detection using a boosted cascade of simple features, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, pp.1063-6919, 2001.
DOI : 10.1109/CVPR.2001.990517