REFERENCES

Amaldi, E. , & Kann, V. (1998). On the approx-imability of minimizing non zero variables or unsatisfied relations in linear systems. Theoretical Computer Science, 209, 237-260. doi:10.1016/S0304-3975(97)00115-1

Amit, Y. , & Geman, D. (1997). Shape quantization and recognition with randomized trees. Neural Computation, 9, 1545-1588. doi:10.1162/neco.1997.9.7.1545

Bellman, R. (1961). Adaptive control processes: A guided tour. Princeton, NJ: Princeton University Press.

Benai'm, M. (2000). Convergence with probability one of stochastic approximation algorithms whose average is cooperative. Nonlinearity, 13(3), 601-616. doi:10.1088/0951-7715/13/3/305

Blum, A. , & Langley, P. (1997). Selection of relevant features and examples in machine learning. Artificial Intelligence, 97(1-2), 245-271. doi:10.1016/S0004-3702(97)00063-5

Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32. doi:10.1023/A:1010933404324

Breiman, L. , Friedman, J. , Stone, C. , & Olshen, R. (1984). Classification and regression tree. San Francisco: Wadsworth International.

Cabot, A. , Engler, H. , & Gadat, S. (2009). On the long time behaviour of second order differential equations with asymptotically small dissipation. In Transactions of the American Mathematical Society.

Candes, E. , & Tao, T. (2005). The dantzig selector: statistical estimation when p is much larger than n. Annals of Statistics, 35, 2313-2351. doi:10.1214/009053606000001523

Duflo, M. (1996). Algorithms stochastiques. Berlin: Springer-Verlag.

Dupuis, P. , & Ishii, H. (1991). On Lipschitz continuity of the solution mapping to the Skorokhod problem, with applications. Stochastics and Stochastics Reports, 35(1), 31-62.

Dupuis, P. , & Ramanan, K. (1999). Convex duality and the Skorokhod problem (Part I & II). Probability Theory and Related Fields, 115(2), 153-236. doi:10.1007/s004400050269

Elisseeff, A. , Weston, J. , & Scholkopf, B. (2003). Use of the zero-norm with linear models and kernel methods. Journal of Machine Learning Research, 3, 1439-1461. doi:10.1162/153244303322753751

Fleuret, F. , & Geman, D. (2001). Coarse-to-fine face detection. International Journal of Computer Vision, 41, 85-107. doi:10.1023/A:1011113216584

Friedman, J. , Hastie, T. , & Tibshirani, R. (2000). Additive logistic regression: a statistical view of boosting. Annals of Statistics, 28, 337-374. doi:10.1214/aos/1016218223

Gadat, S. (2008). Jump diffusion over feature space for object recognition. SIAM Journal on Control and Optimization, 47(2), 904-935. doi:10.1137/060656759

Gadat, S. , & Younes, L. (2007). A stochastic algorithm for feature selection in pattern recognition. Journal of Machine Learning Research, 8, 509-547.

Geman, S. , Bienenstock, E. , & Doursat, R. (1992). Neural networks and the bias/variance dilemma. Neural Computation, 4, 1-58. doi:10.1162/neco.1992.4.1.1

Guyon, I. , & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157-1182. doi:10.1162/153244303322753616

Guyon, I. , Weston, J. , Barnhill, S. , & Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Machine Learning, 46(1-3), 389-422. doi: 10.1023/A: 1012487302797

Ho, T. K. (1998). The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8), 832-844. doi:10.1109/34.709601

Jolliffe, I. T. (2002). Principal component analysis. New York: Springer.

Jutten, C. , & Herault, J. (1991). Blind separation of sources, part i: An adaptive algorithm based on neuromimetic architecture. Signal Processing, 24(1), 1-10. doi:10.1016/0165-1684(91)90079-X

Kira, K. , & Rendell, L. A. (1992). A practical approach to feature selection. In Proceedings of 9th International Conference on Machine Learning (pp. 249-256).

Kononenko, I. (1994). Estimating attributes: Analysis and extensions of relief. In Proceedings of European Conference on Machine Learning (pp. 171-182).

Lê Cao, K. A. , Gonçalves, O. , Besse, P. , & Gadat, S. (2007). Selection of biologically relevant genes with a wrapper stochastic algorithm. Statistical Applications in Genetics and Molecular Biology, 6(1). doi:10.2202/1544-6115.1312

Rissanen, J. (1983). A universal prior for integers and estimation by minimum description length. Annals of Statistics, 11 (2), 416-431. doi:10.1214/aos/1176346150

Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society. Series B. Methodological, 36, 111-147.

Sun, Y. (2007). Iterative relief for feature weighting: Algorithms, theories, and applications. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29, 1035-1051. doi:10.1109/TPA-MI.2007.1093

Sun, Y. , & Lijun, Y. (2007). A genetic algorithm based feature selection approach for 3d face recognition. Biometrics Symposium in 3D Imaging for Safety and Security, Springer - Computational Imaging and Vision, 35, 95-118.

Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B. Methodological, 58, 267-288.

van Dijck, G. , van Hulle, M. , & Wevers, M. (2004). Genetic algorithm for feature subset selection with exploitation of feature correlations from continuous wavelet transform: a real-case application. International Journal of Computational Intelligence, 1, 1-12.

Vapnik, V. (1998). Statistical learning theory. Adaptive and Learning Systems for Signal Processing, Communications, and Control. New York: John Wiley & Sons Inc.

Wright, J. , Yang, A. , Ganesh, A. Y. , Shankar Sastry, S. , & Ma, Y. (2009). Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31 (2), 210-227. doi:10.1109/TPAMI.2008.79

Xiao, R. , Li, W. , Tian, Y. , & Tang, X. (2006). Joint boosting feature selection for robust face recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 06) (pp. 1415-1422).

Yang, J. , & Li, Y. (2006). Orthogonal relief algorithm for feature selection. Lecture Notes in Computer Science, 4113, 227-234. doi:10.1007/11816157_22

Zou, H. , Hastie, T. , & Tibshirani, R. (2006). Sparse principal component analysis. Journal of Computational and Graphical Statistics, 15, 262-286. doi:10.1198/106186006X113430