J. Audibert and A. Tsybakov, Fast learning rates for plug-in classifiers, The Annals of statistics, vol.35, pp.608-633, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00160849

A. Baíllo, A. Cuevas, and J. A. Cuesta-albertos, Supervised classification for a family of gaussian functional models, Scandinavian Journal of Statistics, vol.38, pp.480-498, 2011.

P. Bartlett, M. Jordan, and J. Mcauliffe, Convexity, classification, and risk bounds, Journal of the American Statistical Association, vol.101, pp.138-156, 2006.

P. Bartlett and S. Mendelson, Empirical minimization, Probab. Theory Related Fields, vol.135, pp.311-334, 2006.

G. Biau, F. Bunea, and M. Wegkamp, Functional classification in hilbert spaces, IEEE Transactions on Information Theory, vol.51, pp.2163-2172, 2005.

G. Biau, B. Cadre, and Q. Paris, Cox process functional learning. Statistical Inference for Stochastic Processes, vol.18, pp.257-277, 2015.
URL : https://hal.archives-ouvertes.fr/hal-00820838

G. Biau, F. Cérou, and A. Guyader, Rates of convergence of the functional k-nearest neighbor estimate, IEEE Transactions on Information Theory, vol.56, pp.2034-2040, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00497199

B. Cadre, Supervised classification of diffusion paths, Math. Methods Statist, vol.22, pp.213-225, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01069973

A. Cuevas, M. Febrero, and R. Fraiman, Robust estimation and classification for functional data via projection-based depth notions, Computational Statistics, vol.22, pp.481-496, 2007.

D. Dacunha-castelle and M. Duflo, Probabilité et Statistiques 2 Problémes á temps mobile, 1983.

M. Delattre, V. Genon-catalot, and A. Samson, Estimation of population parameters in stochastic differential equations with random effects in the diffusion coefficient, ESAIM: Probability and Statistics, vol.19, pp.671-688, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01056917

M. Delattre, V. Genon-catalot, and A. Samson, Mixtures of stochastic differential equations with random effects: application to data clustering, Journal of Statistical Planning and Inference, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01218612

C. Denis, Classification in postural style based on stochastic process modeling. The international journal of biostatistics 10, pp.251-260, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00653316

C. Denis and M. Hebiri, Confidence sets with expected sizes for multiclass classification, J. Mach. Learn. Res, vol.18, issue.102, p.28, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01357850

L. Devroye, L. Györfi, and G. Lugosi, A Probabilistic Theory of Pattern Recognition, 1996.

C. Dion and V. Genon-catalot, Bidimensional random effect estimation in mixed stochastic differential model, Stochastic Inference for Stochastic Processes, vol.18, pp.1-28, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01103303

S. Donnet and A. Samson, A review on estimation of stochastic differential equations for pharmacokinetic/pharmacodynamic models, Advanced Drug Delivery Reviews, vol.65, pp.929-939, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00777774

N. El-karoui, S. Peng, and M. C. Quenez, Backward stochastic differential equations in finance, Mathematical finance, vol.7, pp.1-71, 1997.

V. Genon-catalot and J. Jacod, On the estimation of the diffusion coefficient for multidimensional diffusion processes, Annales de l'IHP Probabilités et statistiques, vol.29, pp.119-151, 1993.

A. Gloter, Discrete sampling of an integrated diffusion process and parameter estimation of the diffusion coefficient, ESAIM: Probability and Statistics, vol.4, pp.205-227, 2000.

E. Gobet, Lan property for ergodic diffusions with discrete observations, Annales de l'Institut Henri Poincare (B) Probability and Statistics, vol.38, pp.711-737, 2002.

C. Graham and D. Talay, Stochastic simulation and Monte Carlo methods, of Stochastic Modelling and Applied Probability, vol.68, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00954842

B. Gregorutti, B. Michel, and P. Saint-pierre, Grouped variable importance with random forests and application to multiple functional data analysis, Computational Statistics & Data Analysis, vol.90, pp.15-35, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01935926

X. Guyon, Random fields on a network: modeling, statistics, and applications, 1995.

M. Hoffmann, Adaptive estimation in diffusion processes, Stochastic processes and their Applications, vol.79, pp.135-163, 1999.

J. Jacod and A. Shiryaev, Limit Theorems for Stochastic Processes, ser. A Series of Comprehensive Studies in Mathematics, 2003.

N. Jakobsen and M. Munkholt-&-sørensen, Efficient estimation for diffusions sampled at high frequency over a fixed time interval, Bernoulli, vol.23, pp.1874-1910, 2017.

M. Kessler, Estimation of an ergodic diffusion from discrete observations, Scandinavian Journal of Statistics, vol.24, pp.211-229, 1997.

J. Kuelbs and J. Zinn, Limit theorems for quantile and depth regions for stochastic processes, High Dimensional Probability VII, pp.255-280, 2016.

Y. Kutoyants, Statistical Inference for Ergodic Diffusion Processes, 2004.

T. Lange, K. Mosler, and P. Mozharovskyi, Fast nonparametric classification based on data depth, Statistical Papers, vol.55, pp.49-69, 2014.

S. López-pintado and J. Romo, Depth-based classification for functional data, DIMACS Series in Discrete Mathematics and Theoretical Computer Science, vol.72, p.103, 2006.

P. Massart and E. Nédélec, Risk bounds for statistical learning, The Annals of Statistics, vol.34, pp.2326-2366, 2006.

G. Parisi and N. Sourlas, Supersymmetric field theories and stochastic differential equations, 1992.

B. Pires, C. Szepesvari, and M. Ghavamzadeh, Cost-sensitive multiclass classification risk bounds, International Conference on Machine Learning, pp.1391-1399, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00840485

J. O. Ramsay and B. W. Silverman, Applied functional data analysis: methods and case studies, 2007.

E. Schmisser, Penalized nonparametric drift estimation for a multidimensional diffusion process, Statistics, vol.47, pp.61-84, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00358410

A. Tewari and P. Bartlett, On the consistency of multiclass classification methods, Journal of Machine Learning Research, vol.8, pp.1007-1025, 2007.

A. W. Van-der-vaart and J. A. Wellner, Weak convergence. In Weak convergence and empirical processes, pp.16-28, 1996.

V. Vapnik, Statistical learning theory, vol.1, 1998.

J. Wang, J. Chiou, and H. Mueller, Review of functional data analysis, 2015.

N. Yoshida, Asymptotic behavior of m-estimator and related random field for diffusion process, Annals of the Institute of Statistical Mathematics, vol.42, pp.221-251, 1990.
DOI : 10.1007/bf00050834

N. Yoshida, Estimation for diffusion processes from discrete observation, Journal of Multivariate Analysis, vol.41, pp.220-242, 1992.
DOI : 10.1016/0047-259x(92)90068-q
URL : https://doi.org/10.1016/0047-259x(92)90068-q

T. Zhang, Statistical analysis of some multi-category large margin classification methods, Journal of Machine Learning Research, vol.5, pp.1225-1251, 2004.