P. A. Devijver and J. Kittler, Pattern recognition : a statistical approach, 1982.

B. Efron and R. J. Tibshirani, An Introduction to the Bootstrap, 1994.
DOI : 10.1007/978-1-4899-4541-9

R. Bellman, Adaptive control processes: a guided tour, 1961.
DOI : 10.1515/9781400874668

D. François, High-dimensional data analysis: optimal metrics and feature selection, 2006.

B. E. Boser, I. M. Guyon, and V. Vapnik, A training algorithm for optimal margin classifiers, Proceedings of the fifth annual workshop on Computational learning theory , COLT '92, pp.144-152, 1992.
DOI : 10.1145/130385.130401
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.103.1189

L. Hermes and J. M. Buhmann, Feature selection for support vector machines, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, pp.712-715, 2000.
DOI : 10.1109/ICPR.2000.906174

J. Fridrich, Feature-Based Steganalysis for JPEG Images and Its Implications for Future Design of Steganographic Schemes, Information Hiding: 6th International Workshop, pp.67-81, 2004.
DOI : 10.1007/978-3-540-30114-1_6

J. Park and J. Sandberg, Universal Approximation Using Radial-Basis-Function Networks, Neural Computation, vol.2, issue.2, pp.246-257, 1991.
DOI : 10.1109/35.41401

J. Platt, Fast Training of Support Vector Machines using Sequential Minimal Optimization , chapter 12, 1998.

S. Lyu and H. Farid, Detecting Hidden Messages Using Higher-Order Statistics and Support Vector Machines, 5th International Workshop on Information Hiding, 2002.
DOI : 10.1007/3-540-36415-3_22
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.127.1855

M. Verleysen and D. François, The Curse of Dimensionality in Data Mining and Time Series Prediction, IWANN'05 : 8th International Work-Conference on Artificial Neural Network, pp.758-770, 2005.
DOI : 10.1007/11494669_93

M. Verleysen, D. François, G. Simon, and V. Wertz, On the effects of dimensionality on data analysis with neural networks, Artificial Neural Nets Problem solving methods, pp.105-112, 2003.
DOI : 10.1051/ejess:2001114

N. Provos, Defending against statistical steganalysis, 10th USENIX Security Symposium, pp.323-335, 2001.

V. Vapnik, Statistical Learning Theory, 1998.

M. P. Wand and W. Schucany, Gaussian-based kernels, Canadian Journal of Statistics, vol.5, issue.3, pp.197-20410, 1990.
DOI : 10.2307/3315450

P. Christian and G. Casella, Monte Carlo statistical methods, 1999.

Y. Shi, C. Chen, and W. Chen, A Markov Process Based Approach to Effective Attacking JPEG Steganography, ICME'06 : Internation Conference on Multimedia and Expo, pp.9-12, 2006.
DOI : 10.1007/978-3-540-74124-4_17

T. Pevny and J. Fridrich, Merging Markov and DCT features for multi-class JPEG steganalysis, Security, Steganography, and Watermarking of Multimedia Contents IX, 2007.
DOI : 10.1117/12.696774
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.353.37

A. Westfeld and A. Pfitzmann, Attacks on Steganographic Systems, IH '99: Proceedings of the Third International Workshop on Information Hiding, pp.61-76, 2000.
DOI : 10.1007/10719724_5

A. Whitney, A Direct Method of Nonparametric Measurement Selection, IEEE Transactions on Computers, pp.1100-1103, 1971.
DOI : 10.1109/T-C.1971.223410

Y. Miche, B. Roue, A. Lendasse, and P. Bas, A Feature Selection Methodology for Steganalysis, Multimedia Content Representation, Classification and Security, International Workshop, pp.49-56, 2006.
DOI : 10.1007/11848035_9
URL : https://hal.archives-ouvertes.fr/hal-00166578