P. Viola and M. Jones, Robust Real-Time Face Detection, International Journal of Computer Vision, vol.57, issue.2, pp.137-154, 2004.
DOI : 10.1023/B:VISI.0000013087.49260.fb

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.102.9805

O. Chapelle and Y. Chang, Yahoo! learning to rank challenge overview, Yahoo Learning to Rank Challenge (JMLR W&CP), pp.1-24, 2010.

V. Gligorov, A single track HLT1 trigger, 2011.

L. Bourdev and J. Brandt, Robust Object Detection via Soft Cascade, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp.236-243, 2005.
DOI : 10.1109/CVPR.2005.310

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.190.3554

. Same, MDDAG: designing sparse decision DAGs using Markov decision processes, submitted, 2011.

R. Xiao, L. Zhu, and H. J. Zhang, Boosting chain learning for object detection, Ninth IEEE International Conference on Computer Vision, pp.709-715, 2003.

J. Sochman and J. Matas, WaldBoost ??? Learning for Time Constrained Sequential Detection, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp.150-156, 2005.
DOI : 10.1109/CVPR.2005.373

M. Saberian and N. Vasconcelos, Boosting classifier cascades, Advances in Neural Information Processing Systems, pp.2047-2055, 2010.

B. Póczos, Y. Abbasi-yadkori, C. Szepesvári, R. Greiner, and N. Sturtevant, Learning when to stop thinking and do something!, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pp.825-832, 2009.
DOI : 10.1145/1553374.1553480

H. Lee, A. Battle, R. Raina, and A. Y. Ng, Efficient sparse coding algorithms, Advances in Neural Information Processing Systems, pp.801-808, 2007.

M. Ranzato, C. Poultney, S. Chopra, and Y. Lecun, Efficient learning of sparse representations with an energy-based model, Advances in Neural Information Processing Systems 19, pp.1137-1144, 2007.

H. Larochelle and G. Hinton, Learning to combine foveal glimpses with a third-order Boltzmann machine, Advances in Neural Information Processing Systems, pp.1243-1251, 2010.

Y. Freund and L. Mason, The alternating decision tree learning algorithm, Proceedings of the 16th International Conference on Machine Learning, pp.124-133, 1999.

G. Dulac-arnold, L. Denoyer, P. Preux, and P. Gallinari, Datum-Wise Classification: A Sequential Approach to Sparsity, European Conference on Machine Learning, 2011.
DOI : 10.1007/978-3-642-23780-5_34

URL : https://hal.archives-ouvertes.fr/hal-00617913

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

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

G. Neu, A. György, and C. Szepesvári, The online loop-free stochastic shortest-path problem, Proceedings of the 23th Annual Conference on Computational Learning Theory, pp.231-243, 2010.

R. S. Sutton and A. G. Barto, Reinforcement learning: an introduction. Adaptive computation and machine learning, 1998.
DOI : 10.1007/978-1-4615-3618-5

G. A. Rummery and M. Niranjan, On-line Q-learning using connectionist systems, 1994.

. Cs and . Szepesvári, Algorithms for Reinforcement Learning, 2010.

G. Davis, S. Mallat, and M. Avellaneda, Adaptive greedy approximations, Constructive Approximation, vol.21, issue.1, pp.57-98, 1997.
DOI : 10.1007/BF02678430

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.47.7530

V. Ejov, J. Filar, and J. Gondzio, An Interior Point Heuristic for the Hamiltonian Cycle Problem via Markov Decision Processes, Journal of Global Optimization, vol.29, issue.3, pp.315-334, 2004.
DOI : 10.1023/B:JOGO.0000044772.11089.1a