, observé dans les 5 dernières seconde avant le toucher. (a) Vitesse air (sévérité = 0.68) (b)
, Empirical characterization of random forest variable importance measures, Computational Statistics & Data Analysis, vol.52, issue.4, pp.2249-2260, 2008.
DOI : 10.1016/j.csda.2007.08.015
, Theory of reproducing kernels, Transactions of the American Mathematical Society, vol.68, issue.3, pp.337-404, 1950.
DOI : 10.1090/S0002-9947-1950-0051437-7
, Screening and metamodeling of computer experiments with functional outputs. Application to thermal???hydraulic computations, Reliability Engineering & System Safety, vol.107, pp.122-131, 2012.
DOI : 10.1016/j.ress.2011.10.017
URL : https://hal.archives-ouvertes.fr/hal-00525491
, Empirical comparison of tree ensemble variable importance measures, Chemometrics and Intelligent Laboratory Systems, vol.105, issue.2, pp.157-170, 2011.
DOI : 10.1016/j.chemolab.2010.12.004
, Bolasso, Proceedings of the 25th international conference on Machine learning, ICML '08, pp.33-40, 2008.
DOI : 10.1145/1390156.1390161
URL : https://hal.archives-ouvertes.fr/hal-00271289
, On-line handwriting recognition with support vector machines -a kernel approach, Proceeding of the 8th IWFHR, 2002.
, Slope heuristics: overview and implementation, Statistics and Computing, vol.6, issue.2, pp.455-470, 2012.
DOI : 10.1007/s11222-011-9236-1
URL : https://hal.archives-ouvertes.fr/hal-00461639
, Functional supervised classification with wavelets, Annales de l, ISUP, vol.52, pp.61-80, 2008.
, Modélisation statistique de données fonctionnelles, Analyse des données, pp.167-198, 2003.
, Dimensionality reduction via sparse support vector machines, Journal of Machine Learning Research, vol.3, pp.1229-1243, 2003.
, Analysis of a random forests model, Journal of Machine Learning Research, vol.13, pp.1063-1095, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00704947
, Functional Classification in Hilbert Spaces, IEEE Transactions on Information Theory, vol.51, issue.6, pp.2163-2172, 2005.
DOI : 10.1109/TIT.2005.847705
, Consistency of random forests and other averaging classifiers, Journal of Machine Learning Research, vol.9, pp.2015-2033, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00355368
, Gaussian model selection, Journal of the European Mathematical Society, vol.3, issue.3, pp.203-268, 2001.
DOI : 10.1007/s100970100031
, Minimal penalties for gaussian model selection, Probability Theory and Related Fields, pp.33-73, 2006.
, Selection of relevant features and examples in machine learning, Artificial Intelligence, vol.97, issue.1-2, pp.245-271, 1997.
DOI : 10.1016/S0004-3702(97)00063-5
, 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
, Convex optimization, 2004.
, Bagging predictors, Machine Learning, vol.10, issue.2, pp.123-140, 1996.
DOI : 10.1007/BF00058655
, Out-of-bag estimation, 1997.
, Random forests, Machine Learning, vol.45, issue.1, pp.5-32, 2001.
DOI : 10.1023/A:1010933404324
, Consistency for a simple model of random forests, 2004.
, Classification and regression trees, 1984.
, Correlated variables in regression: Clustering and sparse estimation, Journal of Statistical Planning and Inference, vol.143, issue.11, pp.1835-1858, 2013.
DOI : 10.1016/j.jspi.2013.05.019
, Prediction in functional linear regression, The Annals of Statistics, vol.34, issue.5, pp.2159-2179, 2006.
DOI : 10.1214/009053606000000830
, A data-driven block thresholding approach to wavelet estimation, The Annals of Statistics, vol.37, issue.2, pp.569-595, 2009.
DOI : 10.1214/07-AOS538
, Functional linear model, Statistics & Probability Letters, vol.45, issue.1, pp.11-22, 1999.
DOI : 10.1016/S0167-7152(99)00036-X
, Spline estimators for the functional linear model, Statistica Sinica, vol.13, pp.571-592, 2003.
, Estimation in generalized linear models for functional data via penalized likelihood, Journal of Multivariate Analysis, vol.92, issue.1, pp.24-41, 2005.
DOI : 10.1016/j.jmva.2003.08.008
, Selecting Useful Groups of Features in a Connectionist Framework, IEEE Transactions on Neural Networks, vol.19, issue.3, pp.381-396, 2008.
DOI : 10.1109/TNN.2007.910730
, Indices de Sobol généralisés pour variables dépendantes, 2013.
, Generalized Hoeffding-Sobol decomposition for dependent variables - application to sensitivity analysis, Electronic Journal of Statistics, vol.6, issue.0, pp.2420-2448, 2012.
DOI : 10.1214/12-EJS749
URL : https://hal.archives-ouvertes.fr/hal-00649404
, Smote : Synthetic minority over-sampling technique, Journal of Artificial Intelligence Research, vol.16, pp.321-357, 2002.
, Using random forest to learn imbalanced data, 2004.
, Atomic Decomposition by Basis Pursuit, SIAM Journal on Scientific Computing, vol.20, issue.1, pp.33-61, 1998.
DOI : 10.1137/S1064827596304010
, Support-vector networks, Machine Learning, pp.273-297, 1995.
DOI : 10.1007/BF00994018
, An introduction to support Vector Machines : and other kernel-based learning methods, 2000.
DOI : 10.1017/CBO9780511801389
, Robust estimation and classification for functional data via projection-based depth notions, Computational Statistics, vol.28, issue.3, pp.481-496, 2007.
DOI : 10.1007/s00180-007-0053-0
, Local Polynomial Estimation for Sensitivity Analysis on Models With Correlated Inputs, Technometrics, vol.51, issue.4, pp.452-463, 2009.
DOI : 10.1198/TECH.2009.08124
URL : https://hal.archives-ouvertes.fr/hal-00266102
, Ten Lectures on Wavelets, number 61, CBMS/NSF Series in Applied Mathematics, siam, 1992.
, Les analyses factorielles en calcul des probabilités et en statistique : essai d'étude synthétique, Thèse d'état, 1976.
, Asymptotic theory for the principal component analysis of a vector random function: Some applications to statistical inference, Journal of Multivariate Analysis, vol.12, issue.1, pp.136-154, 1982.
DOI : 10.1016/0047-259X(82)90088-4
, La maîtrise des incertitudes dans un contexte industriel ? 1re partie : une approche méthodologique globale basée sur les exemples, Journal de la Société Française de Statistique, vol.147, issue.3, pp.33-71, 2006.
, Uncertainty in Industrial Practice : A Guide to Quantitative Uncertainty Management, 2008.
DOI : 10.1002/9780470770733
, MapReduce, Communications of the ACM, vol.51, issue.1, pp.107-113, 2008.
DOI : 10.1145/1327452.1327492
, M??thodes statistiques et num??riques de l'analyse harmonique, Annales de l'ins????, issue.15, pp.5-101, 1974.
DOI : 10.2307/20075177
, A probabilistic theory of pattern recognition, 1996.
DOI : 10.1007/978-1-4612-0711-5
, Gene selection and classification of microarray data using random forest, BMC Bioinformatics, vol.7, issue.3, 2006.
, Ideal spatial adaptation by wavelet shrinkage, Biometrika, vol.81, issue.3, pp.425-455, 1994.
DOI : 10.1093/biomet/81.3.425
, Adapting to Unknown Smoothness via Wavelet Shrinkage, Journal of the American Statistical Association, vol.31, issue.432, pp.1200-1224, 1995.
DOI : 10.1080/01621459.1979.10481038
, Minimax estimation via wavelet shrinkage, The Annals of Statistics, vol.26, issue.3, pp.879-921, 1998.
DOI : 10.1214/aos/1024691081
, Wavelet shrinkage : asymptopia, Journal of the Royal Statistical Society, Series B, vol.57, pp.301-369, 1995.
, Density estimation by wavelet thresholding, The Annals of Statistics, vol.24, issue.2, pp.508-539, 1996.
DOI : 10.1214/aos/1032894451
, Support vector regression machines, Advances in Neural Information Processing Systems, 1996.
, Multiple SVM-RFE for Gene Selection in Cancer Classification With Expression Data, IEEE Transactions on Nanobioscience, vol.4, issue.3, pp.228-234, 2005.
DOI : 10.1109/TNB.2005.853657
, Regularization networks and support vector machines, Advances in Computational Mathematics, pp.1-50, 2000.
, Functional additive regression, The Annals of Statistics, vol.43, issue.5, 2013.
DOI : 10.1214/15-AOS1346SUPP
, Recent Advances in Functional Data Analysis and Related Topics, 2011.
DOI : 10.1007/978-3-7908-2736-1
URL : https://hal.archives-ouvertes.fr/hal-00794868
, The functional nonparametric model and application to spectrometric data, Computational Statistics, vol.17, issue.4, pp.545-564, 2002.
DOI : 10.1007/s001800200126
, Curves discrimination: a nonparametric functional approach, Computational Statistics & Data Analysis, vol.44, issue.1-2, pp.161-173, 2003.
DOI : 10.1016/S0167-9473(03)00032-X
, Nonparametric Functional Data Analysis : Theory and Practice, 2006.
, Additive prediction and boosting for functional data, Computational Statistics & Data Analysis, vol.53, issue.4, pp.1400-1413, 2009.
DOI : 10.1016/j.csda.2008.11.023
URL : https://hal.archives-ouvertes.fr/hal-00628614
, Discriminatory Analysis. Nonparametric Discrimination: Consistency Properties, International Statistical Review / Revue Internationale de Statistique, vol.57, issue.3, p.477, 1951.
DOI : 10.2307/1403797
, A note on the group lasso and a sparse group lasso, 2010.
, Functional Classification with Margin Conditions, 19th Annual Conference on Learning Theory, 2006.
DOI : 10.1007/11776420_10
URL : https://hal.archives-ouvertes.fr/hal-00457770
, Variance reduction in purely random forests, Journal of Nonparametric Statistics, vol.2, issue.3, pp.543-562, 2012.
DOI : 10.1007/978-0-387-84858-7
, Variable selection using random forests, Pattern Recognition Letters, vol.31, issue.14, pp.2225-2236, 2010.
DOI : 10.1016/j.patrec.2010.03.014
URL : https://hal.archives-ouvertes.fr/hal-00755489
, Variable selection in generalized functional linear models, Stat, vol.101, issue.3, pp.86-101, 2013.
DOI : 10.1002/sta4.20
, Bornes de risque, détection de ruptures, boosting : trois thèmes autour de CART en régression, 2002.
, Model Selection for CART Regression Trees, IEEE Transactions on Information Theory, vol.51, issue.2, pp.658-670, 2005.
DOI : 10.1109/TIT.2004.840903
URL : https://hal.archives-ouvertes.fr/hal-00326549
, Bootstrap in functional linear regression, Journal of Statistical Planning and Inference, vol.141, issue.1, pp.453-461, 2011.
DOI : 10.1016/j.jspi.2010.06.027
, Nonparametric regression and generalized linear models : a roughness penalty approach, Chapman and Hall, 1994.
DOI : 10.1007/978-1-4899-4473-3
, Correlation and variable importance in random forests, Statistics and Computing, vol.2, issue.1, 2014.
DOI : 10.1007/s11222-016-9646-1
URL : https://hal.archives-ouvertes.fr/hal-00879978
, Grouped variable importance with random forests and applications to multivariate functional data analysis, 2014.
, Variable Importance Assessment in Regression: Linear Regression versus Random Forest, The American Statistician, vol.63, issue.4, pp.308-319, 2009.
DOI : 10.1198/tast.2009.08199
, An introduction to variable and feature selection, Journal of Machine Learning Research, vol.3, pp.1157-1182, 2003.
, Gene selection for cancer classification using support vector machines, Machine Learning, vol.46, pp.1-3, 2002.
, On the minimax optimality of block thresholded wavelet estimators, Statistica Sinica, vol.9, pp.33-49, 1999.
, A new variable selection approach using Random Forests, Computational Statistics & Data Analysis, vol.60, pp.50-69, 2013.
DOI : 10.1016/j.csda.2012.09.020
, Generalized Additive Models, Statistical Science, vol.1, issue.3, pp.297-318, 1986.
DOI : 10.1214/ss/1177013604
, Classification by pairwise coupling, The Annals of Statistics, vol.26, issue.2, pp.451-471, 1998.
DOI : 10.1214/aos/1028144844
, The Elements of Statistical Learning, 2001.
, The Influence of Feature Selection Methods on Accuracy, Stability and Interpretability of Molecular Signatures, PLoS ONE, vol.66, issue.12, pp.1-12, 2011.
DOI : 10.1371/journal.pone.0028210.t003
URL : https://hal.archives-ouvertes.fr/hal-00559580
, Improving stability and interpretability of gene expression signatures, 2010.
, Stable feature selection for biomarker discovery, Computational Biology and Chemistry, vol.34, issue.4, pp.215-225, 2010.
DOI : 10.1016/j.compbiolchem.2010.07.002
, A Class of Statistics with Asymptotically Normal Distribution, The Annals of Mathematical Statistics, vol.19, issue.3, pp.293-325, 1948.
DOI : 10.1214/aoms/1177730196
, Importance measures in global sensitivity analysis of nonlinear models, Reliability Engineering & System Safety, vol.52, issue.1, pp.1-17, 1996.
DOI : 10.1016/0951-8320(96)00002-6
, Inference for Functional Data with Applications, 2012.
DOI : 10.1007/978-1-4614-3655-3
, Multivariate functional clustering for the morphological analysis of electrocardiograph curves, Journal of the Royal Statistical Society: Series C (Applied Statistics), vol.27, issue.3, pp.401-418, 2012.
DOI : 10.1111/j.1467-9876.2012.01062.x
, A Review on Global Sensitivity Analysis Methods, 2014.
DOI : 10.1007/978-1-4899-7547-8_5
URL : https://hal.archives-ouvertes.fr/hal-00975701
, Response surfaces and sensitivity analyses for an environmental model of dose calculations, Reliability Engineering & System Safety, vol.91, issue.10-11, pp.1241-1251, 2006.
DOI : 10.1016/j.ress.2005.11.021
, Variable importance in binary regression trees and forests, Electronic Journal of Statistics, vol.1, issue.0, pp.519-537, 2007.
DOI : 10.1214/07-EJS039
, Group lasso with overlap and graph lasso, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pp.433-440, 2009.
DOI : 10.1145/1553374.1553431
, Principal component models for sparse functional data, Biometrika, vol.87, issue.3, pp.587-602, 2000.
DOI : 10.1093/biomet/87.3.587
, An AUC-based permutation variable importance measure for random forests, BMC Bioinformatics, vol.14, issue.1, p.119, 2013.
DOI : 10.1186/1471-2105-11-110
, Joint analysis of two microarray gene-expression data sets to select lung adenocarcinoma marker genes, BMC Bioinformatics, vol.5, issue.1, p.81, 2004.
DOI : 10.1186/1471-2105-5-81
, Wavelet Threshold Estimators for Data with Correlated Noise, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.59, issue.2, pp.319-351, 1997.
DOI : 10.1111/1467-9868.00071
, Stability of feature selection algorithms: a study on high-dimensional spaces, Knowledge and Information Systems, vol.20, issue.1???2, pp.95-116, 2007.
DOI : 10.1007/s10115-006-0040-8
, Wrappers for feature subset selection, Artificial Intelligence, vol.97, issue.1-2, pp.273-324, 1997.
DOI : 10.1016/S0004-3702(97)00043-X
URL : http://doi.org/10.1016/s0004-3702(97)00043-x
, Improving stability of feature selection methods, Computer Analysis of Images and Patterns, pp.929-936, 2007.
, Non-asymptotic oracle inequalities for the Lasso and Group Lasso in high dimensional logistic model, ESAIM: Probability and Statistics, vol.20, 2012.
DOI : 10.1051/ps/2015020
URL : https://hal.archives-ouvertes.fr/hal-00703714
, A k-nearest neighbor approach for functional regression, Statistics & Probability Letters, vol.78, issue.10, pp.1189-1193, 2008.
DOI : 10.1016/j.spl.2007.11.014
, Adaptive estimation of a quadratic functional of a density by model selection, ESAIM: Probability and Statistics, vol.9, pp.1245-1501, 2000.
DOI : 10.1051/ps:2005001
, A Survey on Filter Techniques for Feature Selection in Gene Expression Microarray Analysis, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.9, issue.4, pp.1106-1119, 2012.
DOI : 10.1109/TCBB.2012.33
, Classification using functional data analysis for temporal gene expression data, Bioinformatics, vol.22, issue.1, pp.68-76, 2006.
DOI : 10.1093/bioinformatics/bti742
, Depth-based classification for functional data, DIMACS Series in Discrete Mathematics and Theoretical Computer Science 72 : 103. Références bibliographiques, 2006.
, Variable selection in kernel Fisher discriminant analysis by means of recursive feature elimination, Computational Statistics & Data Analysis, vol.51, issue.3, pp.2043-2055, 2006.
DOI : 10.1016/j.csda.2005.12.018
, A theory for multiresolution signal decomposition : the wavelet representation , Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.11, issue.7, pp.674-693, 1989.
, Une exploration des signaux en ondelettes, Éditions de l'École Poly- technique, 2000.
, A Wavelet Tour of Signal Processing, 2008.
, Some comments on cp, pp.661-675, 1973.
, Variance-based sensitivity indices for models with dependent inputs, Reliability Engineering & System Safety, vol.107, pp.115-121, 2012.
DOI : 10.1016/j.ress.2011.08.008
URL : https://hal.archives-ouvertes.fr/hal-01093038
, Concentration inequalities and model selection, 2003.
, Variable and boundary selection for functional data via multiclass logistic regression modeling, Computational Statistics & Data Analysis, vol.78, issue.0, pp.176-185, 2014.
DOI : 10.1016/j.csda.2014.04.015
, Variable selection for functional regression models via the regularization, Computational Statistics & Data Analysis, vol.55, issue.12, pp.3304-3310, 2011.
DOI : 10.1016/j.csda.2011.06.016
, The group lasso for logistic regression, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.68, issue.1, pp.53-71, 2008.
DOI : 10.1111/j.1467-9868.2007.00627.x
, Stability selection, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.7, issue.4, pp.417-473, 2010.
DOI : 10.1111/j.1467-9868.2010.00740.x
, Functions of positive and negative type, and their connection with the theory of integral equations, Philosophical Transactions of the, pp.415-446, 1909.
, Décomposition par ondelettes et méthodes comparatives : étude d'une courbe de charge électrique, pp.55-77, 1994.
, Multivariate denoising methods combining wavelets and principal component analysis for mass spectrometry data, PROTEOMICS, vol.1, issue.14, pp.2564-2572, 2010.
DOI : 10.1002/pmic.200900185
, Controversy of variable importance in random forests, Journal of Unified Statistical Techniques, vol.1, pp.15-20, 2013.
, Letter to the Editor: On the stability and ranking of predictors from random forest variable importance measures, Briefings in Bioinformatics, vol.12, issue.4, pp.369-373, 2011.
DOI : 10.1093/bib/bbr016
, Predictor correlation impacts machine learning algorithms: implications for genomic studies, Bioinformatics, vol.25, issue.15, pp.1884-1890, 2009.
DOI : 10.1093/bioinformatics/btp331
, The behaviour of random forest permutation-based variable importance measures under predictor correlation, BMC Bioinformatics, vol.11, issue.1, p.110, 2010.
DOI : 10.1186/1471-2105-11-110
, Group lasso with overlaps : the latent group lasso approach, 2009.
URL : https://hal.archives-ouvertes.fr/inria-00628498
, Fusso : Functional shrinkage and selection operator, Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, pp.715-723, 2014.
, Support vector machines : Training and applications, 1997.
, Wavelet Methods for Time Series Analysis, 2000.
, Wavelets in functional data analysis: Estimation of multidimensional curves and their derivatives, Computational Statistics & Data Analysis, vol.56, issue.6, pp.1482-1498, 2012.
DOI : 10.1016/j.csda.2011.12.016
, Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods Advances in Large Margin Classifiers, pp.61-74, 1999.
, Classification supervisée en grande dimension. application à l'agrément de conduite automobile, Revue de Statistique Appliquée, vol.4, pp.39-58, 2006.
, Variable selection using svm based criteria, Journal of Machine Learning Research, vol.3, pp.1357-1370, 2003.
, Functional Data Analysis, Series in Statistics Series, 1997.
, Applied Functional Data Analysis : Methods and Case Studies, 2002.
DOI : 10.1007/b98886
, Functional Data Analysis, 2005.
, Structured penalties for functional linear models???partially empirical eigenvectors for regression, Electronic Journal of Statistics, vol.6, issue.0, pp.323-353, 2012.
DOI : 10.1214/12-EJS676
, Linear statistical inference and its applications, Wiley series in probability and mathematical statistics : Probability and mathematical statistics, 1973.
, A Functional Approach to Variable Selection in Spectrometric Problems, Proceedings of 16th International Conference on Artificial Neural Networks, ICANN 2006, pp.11-20, 2006.
DOI : 10.1007/11840817_2
, Support vector machine for functional data classification, Neurocomputing, vol.69, issue.7-9, pp.730-742, 2006.
DOI : 10.1016/j.neucom.2005.12.010
URL : https://hal.archives-ouvertes.fr/hal-00144141
, Recent Advances in the Use of SVM for Functional Data Classification, Proceedings of 1rst International Workshop on Functional and Operatorial Statistics, 2008.
DOI : 10.1007/978-3-7908-2062-1_41
URL : https://hal.archives-ouvertes.fr/hal-00635480
, Making best use of model evaluations to compute sensitivity indices, Computer Physics Communications, vol.145, issue.2, pp.280-297, 2002.
DOI : 10.1016/S0010-4655(02)00280-1
, Sensitivity Analysis, 2009.
URL : https://hal.archives-ouvertes.fr/inria-00386559
, Ridge regression learning algorithm in dual variables, Proceedings of the 15th International Conference on Machine Learning, pp.515-521, 1998.
, Learning with Kernels : Support Vector Machines, Regularization, Optimization, and Beyond, 2001.
, Estimating the Dimension of a Model, The Annals of Statistics, vol.6, issue.2, pp.461-464, 1978.
DOI : 10.1214/aos/1176344136
, Consistency of random forests, The Annals of Statistics, vol.43, issue.4, 2014.
DOI : 10.1214/15-AOS1321SUPP
URL : https://hal.archives-ouvertes.fr/hal-00990008
, Variable selection with error control: another look at stability selection, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.97, issue.1, pp.55-80, 2013.
DOI : 10.1111/j.1467-9868.2011.01034.x
, Kernel Methods for Pattern Analysis, 2004.
DOI : 10.1017/CBO9780511809682
, Dynamic time-alignment kernel in support vector machine, Advances in Neural Information Processing Systems 14, pp.921-928, 2001.
, A Sparse-Group Lasso, Journal of Computational and Graphical Statistics, vol.67, issue.2, 2013.
DOI : 10.1080/10618600.2012.681250
, Standardization and the Group Lasso Penalty, Statistica Sinica, vol.22, issue.3, pp.983-1001, 2012.
DOI : 10.5705/ss.2011.075
, Sensitivity estimates for non linear mathematical models, Mathematical Modelling and Computational Experiments, vol.1, pp.407-414, 1993.
, Optimal classification for time-course gene expression data using functional data analysis, Computational Biology and Chemistry, vol.32, issue.6, pp.426-432, 2008.
DOI : 10.1016/j.compbiolchem.2008.07.007
, Support vector regression with anova decomposition kernels Advances in kernel methods, pp.285-292, 1997.
, Conditional Variable Importance for Random Forests, BMC Bioinformatics, vol.9, issue.1, p.307, 2008.
DOI : 10.1186/1471-2105-9-307
, Danger : High power ! exploring the statistical properties of a test for random forest variable importance, Proceedings of the 18th International Conference on Computational Statistics, 2008.
, Weighted least squares support vector machines: robustness and sparse approximation, Neurocomputing, vol.48, issue.1-4, pp.85-105, 2002.
DOI : 10.1016/S0925-2312(01)00644-0
, Application of Breiman???s Random Forest to Modeling Structure-Activity Relationships of Pharmaceutical Molecules, 2004.
DOI : 10.1007/978-3-540-25966-4_33
, Interpretable dimension reduction for classifying functional data, Computational Statistics & Data Analysis, vol.57, issue.1, pp.282-296, 2013.
DOI : 10.1016/j.csda.2012.06.017
, Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society, Series B, vol.58, pp.267-288, 1996.
, Solution of incorrectly formulated problems and the regularization method, Soviet Mathematics Doklady pp, pp.1035-1038, 1963.
, Classification with correlated features: unreliability of feature ranking and solutions, Bioinformatics, vol.27, issue.14, pp.1986-1994, 2011.
DOI : 10.1093/bioinformatics/btr300
, Sélection de variables pour la discrimination en grande dimension et classification de données fonctionnelles, 2005.
, High-dimensional generalized linear models and the lasso, The Annals of Statistics, vol.36, issue.2, pp.614-645, 2008.
DOI : 10.1214/009053607000000929
, Statistical Inference for Variable Importance, The International Journal of Biostatistics, vol.2, issue.1, pp.1-33, 2006.
DOI : 10.2202/1557-4679.1008
, The nature of statistical learning theory, 1995.
, Statistical Learning Theory, 1998.
, Controlling the sensitivity of support vector machines, Proceedings of the International Joint Conference on AI, pp.55-60, 1999.
, Global sensitivity analysis for a numerical model of radionuclide migration from the RRC ???Kurchatov Institute??? radwaste disposal site, Stochastic Environmental Research and Risk Assessment, vol.16, issue.1, pp.17-31, 2008.
DOI : 10.1007/s00477-006-0093-y
, All of Nonparametric Statistics, 2006.
, Ecient sparse group feature selection via nonconvex optimization, Proceedings of the 30th International Conference on Machine Learning, pp.284-292, 2013.
, On predicting rare classes with svm ensembles in scene classification, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp.21-24, 2003.
, A supervised feature subset selection technique for multivariate time series, Proceedings of the Workshop on Feature Selection for Data Mining : Interfacing Machine Learning with Statistics, 2005.
, Feature Subset Selection on Multivariate Time Series with Extremely Large Spatial Features, Sixth IEEE International Conference on Data Mining, Workshops (ICDMW'06), pp.337-342, 2006.
DOI : 10.1109/ICDMW.2006.81
, Model selection and estimation in regression with grouped variables, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.58, issue.1, pp.49-67, 2006.
DOI : 10.1198/016214502753479356
, Model selection and estimation in regression with grouped variables, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.58, issue.1, pp.49-67, 2006.
DOI : 10.1198/016214502753479356
, Variable selection for the multicategory SVM via adaptive sup-norm regularization, Electronic Journal of Statistics, vol.2, issue.0, pp.149-167, 2008.
DOI : 10.1214/08-EJS122
, The composite absolute penalties family for grouped and hierarchical variable selection, The Annals of Statistics, vol.37, issue.6A, pp.3468-3497, 2009.
DOI : 10.1214/07-AOS584
, Group variable selection via a hierarchical lasso and its oracle property, Statistics and Its Interface, vol.3, issue.4, pp.557-574, 2010.
DOI : 10.4310/SII.2010.v3.n4.a13
, A functional generalized linear model with curve selection in cervical pre-cancer diagnosis using fluorescence spectroscopy, Lecture Notes- Monograph Series pp, pp.173-189, 2009.
, Classification of gene microarrays by penalized logistic regression, Biostatistics, vol.5, issue.3, pp.427-443, 2004.
DOI : 10.1093/biostatistics/kxg046
, 1-norm support vector machines, Advances in Neural Information Processing Systems, 2004.
, Reinforcement Learning Trees, Journal of the American Statistical Association, vol.7, issue.512, 2012.
DOI : 10.1080/01621459.2011.637468
, The Adaptive Lasso and Its Oracle Properties, Journal of the American Statistical Association, vol.101, issue.476, pp.1418-1429, 2006.
DOI : 10.1198/016214506000000735
, Regularization and variable selection via the elastic net, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.5, issue.2, pp.301-320, 2005.
DOI : 10.1073/pnas.201162998