H. Almuallim and T. G. Dietterich, Learning Boolean concepts in the presence of many irrelevant features, Artificial Intelligence, vol.69, issue.1-2, pp.279-305, 1994.
DOI : 10.1016/0004-3702(94)90084-1

A. Blumer, A. Ehrenfeucht, D. Haussler, and M. K. Warmuth, Occam's Razor, Information Processing Letters, vol.24, issue.6, pp.377-380, 1987.
DOI : 10.1016/0020-0190(87)90114-1

T. Pierrebessì-ere, . Laplace, and . Group, Survey : Probabilistic methodology and techniques for artefact conception and development, 2003.

O. François, Notes de cours de réseaux de neurones, 2001.

P. Domingos and M. J. Pazzani, On the optimality of the simple bayesian classifier under zero-one loss, Machine Learning, vol.29, issue.2/3, pp.103-130, 1997.
DOI : 10.1023/A:1007413511361

O. Gaudoin, Méthodes statistiques pour l'ingénieur. grenoble, france, 2002.

E. Edwin and . Ghiselli, Theory of Psychological Measurement, 1964.

]. M. Hal98 and . Hall, Correlation-based feature selection for machine learning, 1998.

. Hal00, A. Mark, and . Hall, Correlation-based feature selection for discrete and numeric class machine learning, Proc. 17th International Conf. on Machine Learning, pp.359-366, 2000.

D. Heckerman, D. Geiger, and D. M. Chickering, Learning bayesian networks : The combination of knowledge and statistical data, In KDD Workshop, pp.85-96, 1994.

J. Holland, Adaptation in Natural and Artificial Systems, 1975.

A. Elisseeff and I. Guyon, An introduction to variable and feature selection, Journal of Machine Learning Research, 2003.

G. H. John, R. Kohavi, and K. Pfleger, Irrelevant Features and the Subset Selection Problem, International Conference on Machine Learning, pp.121-129, 1994.
DOI : 10.1016/B978-1-55860-335-6.50023-4

R. Kohavi and G. H. John, Wrappers for feature subset selection, Artificial Intelligence, vol.97, issue.1-2, pp.273-324, 1997.
DOI : 10.1016/S0004-3702(97)00043-X

L. [. Kullback, On information and suoeciency, p.7986, 1951.

[. Kendall and A. Stewart, The Advanced Theory of Statistics, 1977.

D. Koller and M. Sahami, Toward optimal feature selection, International Conference on Machine Learning, pp.284-292, 1996.

J. Diard, O. Lebeltel, P. Bessì-ere, and E. Mazer, Bayesian robots programming, Autonomous Robot, 2003.

R. [. Liu and . Setiono, Chi2 : Feature selection and discretization of numeric attributes, 1995.

L. [. Liu and . Yu, Feature selection for data mining, 2002.

D. Margartitis and S. Thrun, A bayesian multiresolution independence test for continuous variables, Uncertainty in Artificial Intelligence : Proceedings of the Seventeenth Conference (UAI-2001), pp.346-353, 2001.

K. [. Narendra and . Fukunaga, A Branch and Bound Algorithm for Feature Subset Selection, IEEE Transactions on Computers, pp.917-922, 1977.
DOI : 10.1109/TC.1977.1674939

O. Ritthoff, A hybrid approach to feature selection and generation using an ea, 2002.

B. D. Ripley, Pattern Recognition and Neural Networks, 1996.
DOI : 10.1017/CBO9780511812651

A. Spalanzani, AlgorithmesévolutionnairesAlgorithmesévolutionnaires pour l'´ etude de la robustesse des systèmes de reconnaissance de la parole, thèse de l'université joseph fourier, 1999.

R. David and . Wolf, Mutual information as a bayesian measure of independence, 1994.

J. Yang and V. Honavar, Feature subset selection using a genetic algorithm, IEEE Intelligent Systems, vol.13, issue.2, pp.44-49, 1998.
DOI : 10.1109/5254.671091

M. Zaffalon and M. Hutter, Robust feature selection by mutual information distributions, 2002.