L. Breiman, Random forests, Machine learning, vol.45, issue.1, pp.5-32, 2001.

D. H. Wolpert, Stacked generalization, Neural Networks, vol.5, issue.2, pp.241-259, 1992.

J. Gama and P. Brazdil, Cascade generalization, Machine Learning, vol.41, issue.3, pp.315-343, 2000.

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.

J. H. Friedman, Greedy function approximation: a gradient boosting machine, Annals of statistics, pp.1189-1232, 2001.

T. Chen and C. Guestrin, Xgboost: A scalable tree boosting system, pp.785-794, 2016.

G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen et al., Lightgbm: A highly efficient gradient boosting decision tree, 2017.

N. C. Oza, Online bagging and boosting, Systems, man and cybernetics, vol.3, pp.2340-2345, 2005.
DOI : 10.1109/icsmc.2005.1571498

URL : http://hdl.handle.net/2060/20050239012

H. Grabner and H. Bischof, On-line boosting and vision, IEEE, vol.1, pp.260-267, 2006.
DOI : 10.1109/cvpr.2006.215

S. T. Chen, H. T. Lin, and C. J. Lu, An online boosting algorithm with theoretical justifications, 2012.

A. Beygelzimer, S. Kale, and H. Luo, Optimal and adaptive algorithms for online boosting, 2015.

Y. H. Jung, J. Goetz, and A. Tewari, Online multiclass boosting, Advances in Neural Information Processing Systems, pp.920-929, 2017.

A. Beygelzimer, E. Hazan, S. Kale, and H. Luo, Online gradient boosting, Advances in Neural Information Processing Systems, pp.2458-2466, 2015.

H. Hu, W. Sun, A. Venkatraman, M. Hebert, and J. A. Bagnell, Gradient boosting on stochastic data streams, pp.595-603, 2017.

N. García-pedrajas, C. García-osorio, and C. Fyfe, Nonlinear boosting projections for ensemble construction, Journal of Machine Learning Research, vol.8, pp.1-33, 2007.

C. J. Becker, C. M. Christoudias, and P. Fua, Non-linear domain adaptation with boosting, Advances in Neural Information Processing Systems, pp.485-493, 2013.

O. Chapelle, P. Shivaswamy, S. Vadrevu, K. Weinberger, Y. Zhang et al., Boosted multi-task learning, Machine learning, vol.85, issue.1-2, pp.149-173, 2011.
DOI : 10.1007/s10994-010-5231-6

M. Scholz and R. Klinkenberg, Boosting classifiers for drifting concepts, Intelligent Data Analysis, vol.11, issue.1, pp.3-28, 2007.
DOI : 10.3233/ida-2007-11102

URL : https://eldorado.tu-dortmund.de/bitstream/2003/22236/1/tr06-06.pdf

S. Han, Z. Meng, A. S. Khan, and Y. Tong, Incremental boosting convolutional neural network for facial action unit recognition, In: NIPS, pp.109-117, 2016.

M. Opitz, G. Waltner, H. Possegger, and H. Bischof, Bier-boosting independent embeddings robustly, pp.5189-5198, 2017.
DOI : 10.1109/iccv.2017.555

C. Leistner, A. Saffari, P. Roth, and H. Bischof, On robustness of on-line boostinga competitive study, 3rd ICCV Workshop on On-line Computer Vision, 2009.

W. Gao, R. Jin, S. Zhu, and Z. H. Zhou, One-pass auc optimization, International Conference on Machine Learning, pp.906-914, 2013.
DOI : 10.1016/j.artint.2016.03.003

URL : https://manuscript.elsevier.com/S0004370216300261/pdf/S0004370216300261.pdf

A. Blum, A. Kalai, and J. Langford, Beating the hold-out: Bounds for k-fold and progressive cross-validation, COLT, ACM, pp.203-208, 1999.