P. Atrey, M. Hossain, E. Saddik, A. Kankanhalli, and M. , Multimodal fusion for multimedia analysis: a survey, Multimedia Systems, vol.24, issue.11, pp.345-379, 2010.
DOI : 10.1007/s00530-010-0182-0

L. Breiman, Bagging predictors, Machine Learning, vol.10, issue.2, pp.123-140, 1996.
DOI : 10.1007/BF00058655

L. Breiman, Random forests, Machine Learning, vol.45, issue.1, pp.5-32, 2001.
DOI : 10.1023/A:1010933404324

L. Devroye, L. Györfi, and G. Lugosi, A Probabilistic Theory of Pattern Recognition Ensemble methods in machine learning, Multiple Classifier Systems, pp.1-15, 1996.

P. Domingos, Bayesian averaging of classifiers and the overfitting problem, International Conference on Machine Learning, p.223230, 2000.

R. Duda, P. Hart, and D. Stork, Pattern classification. Pattern Classification and Scene Analysis: Pattern Classification, Wiley, URL books.google.fr/books?id= YoxQAAAAMAAJ Floyd S, Warmuth MK (1995) Sample Compression, Learnability, and the Vapnik- Chervonenkis Dimension, Machine Learning, vol.21, issue.3, pp.269-304, 2001.

Y. Freund and R. Schapire, Experiments with a new boosting algorithm, International Conference on Machine Learning, pp.148-156, 1996.

P. Germain, A. Lacoste, F. Laviolette, M. Marchand, S. Shanian et al., A PAC-Bayes Sample Compression Approach to Kernel Methods PAC-Bayesian Compression Bounds on the Prediction Error of Learning Algorithms for Classification, International Conference on Machine Learning, pp.55-76, 2005.

D. Haussler, M. Kearns, and R. Schapire, Bounds on the sample complexity of bayesian learning using information theory and the vc dimension, Machine Learning, vol.14, issue.1, pp.83-113, 1994.

D. Kedem, S. Tyree, K. Weinberger, F. Sha, and G. Lanckriet, Non-linear Metric Learning, Advances in Neural Information Processing Systems, pp.2582-2590, 2012.

A. Lacasse, F. Laviolette, M. Marchand, P. Germain, and N. Usunier, PAC-Bayes Bounds for the Risk of the Majority Vote and the Variance of the Gibbs Classifier, Advances in Neural Information Processing Systems Laviolette F, Marchand M (2007) PAC-Bayes Risk Bounds for Stochastic Averages and Majority Votes of Sample-Compressed Classifiers, pp.1461-1487, 2007.

F. Laviolette, M. Marchand, J. Roy, F. Laviolette, and J. Shawe-taylor, From PAC-Bayes Bounds to Quadratic Programs for Majority Votes Tighter pac-bayes bounds through distribution-dependent priors, International Conference on Machine Learning Lever G, pp.4-28, 2011.

M. Marchand and M. Sokolova, Learning with Decision Lists of Data-Dependent Features, Journal of Machine Learning Research, vol.6, pp.427-451, 2005.

A. Maurer, A Note on the PAC Bayesian Theorem. CoRR cs, 2004.

D. Mcallester, PAC-Bayesian model averaging, Proceedings of the twelfth annual conference on Computational learning theory , COLT '99, pp.164-170, 1999.
DOI : 10.1145/307400.307435

D. Mcallester, Simplified PAC-Bayesian Margin Bounds, Annual Conference on Learning Theory, pp.203-215, 2003.
DOI : 10.1007/978-3-540-45167-9_16

E. Morvant, S. Koço, L. Ralaivola, A. Habrard, and S. Ayache, PAC-Bayesian Generalization Bound on Confusion Matrix for Multi-Class Classification Majority Vote of Diverse Classifiers for Late Fusion A Simple Locally Adaptive Nearest Neighbor Rule With Application To Pollution Forecasting, International Conference on Machine Learning Morvant E IAPR Joint International Workshops on Statistical Techniques in Pattern Recognition and Structural and Syntactic Pattern Recognition, pp.1369-1382, 2003.

A. Opelt, M. Fussenegger, A. Pinz, and P. Auer, Weak Hypotheses and Boosting for Generic Object Detection and Recognition, European Conference on Computer Vision, pp.71-84, 2004.
DOI : 10.1007/978-3-540-24671-8_6
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.219.3175

M. Re and G. Valentini, Ensemble Methods, pp.563-582, 2012.
DOI : 10.1201/b11822-34

R. Schapire and Y. Singer, Improved boosting algorithms using confidence-rated predictions, Proceedings of the eleventh annual conference on Computational learning theory , COLT' 98, p.297336, 1999.
DOI : 10.1145/279943.279960
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.156.2440

S. Sun, A survey of multi-view machine learning, Neural Computing and Applications, vol.43, issue.7-8, pp.2031-2038, 2013.
DOI : 10.1007/s00521-013-1362-6

K. Weinberger and L. Saul, Distance Metric Learning for Large Margin Nearest Neighbor Classification, Journal of Machine Learning Research, vol.10, pp.207-244, 2009.

C. Xu, D. Tao, and C. Xu, A Survey on Multi-view Learning, Tech. rep, 2013.

A. Appendices and A. , 1 Tools Theorem 4 (Markov's inequality) Let Z be a random variable and t ? 0