F. Auger and P. Flandrin, Improving the readability of time-frequency and time-scale representations by the reassignment method, IEEE Transactions on Signal Processing, vol.43, issue.5, pp.1068-1089, 1995.
DOI : 10.1109/78.382394

D. Jones and R. Baraniuk, An adaptive optimal-kernel time-frequency representation, IEEE Transactions on Signal Processing, vol.43, issue.10, pp.2361-2371, 1995.
DOI : 10.1109/78.469854

J. Gosme, C. Richard, and P. Gonçalvès, Adaptive diffusion as a versatile tool for time-frequency and time-scale representations processing: a review, IEEE Transactions on Signal Processing, vol.53, issue.11, pp.4136-4146, 2005.
DOI : 10.1109/TSP.2005.857048

URL : https://hal.archives-ouvertes.fr/inria-00570601

P. Flandrin, A time-frequency formulation of optimum detection, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.36, issue.9, pp.1377-1384, 1988.
DOI : 10.1109/29.90365

A. Sayeed and D. Jones, Optimal detection using bilinear time-frequency and time-scale representations, IEEE Transactions on Signal Processing, vol.43, issue.12, pp.2872-2883, 1995.
DOI : 10.1109/78.476431

N. Aronszajn, 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

B. Boser, I. 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

B. Schölkopf, A. Smola, and K. Müller, Nonlinear Component Analysis as a Kernel Eigenvalue Problem, Neural Computation, vol.20, issue.5, pp.1299-1319, 1998.
DOI : 10.1007/BF02281970

S. Mika, G. Rätsch, J. Weston, B. Schölkopf, K. Müller et al., Fisher discriminant analysis with kernels, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468), pp.41-48, 1999.
DOI : 10.1109/NNSP.1999.788121

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

G. Baudat and F. Anouar, Generalized Discriminant Analysis Using a Kernel Approach, Neural Computation, vol.25, issue.10, pp.2385-2404, 2000.
DOI : 10.1016/0893-6080(90)90049-Q

V. N. Vapnik, The Nature of Statistical Learning Theory, 1995.

F. Cucker and S. Smale, On the mathematical foundations of learning, Bulletin of the American Mathematical Society, vol.39, issue.01, pp.1-49, 2001.
DOI : 10.1090/S0273-0979-01-00923-5

M. Davy, A. Gretton, A. Doucet, and P. Rayner, Optimized support vector machines for nonstationary signal classification, IEEE Signal Processing Letters, vol.9, issue.12, pp.442-445, 2002.
DOI : 10.1109/LSP.2002.806070

A. Rakotomamonjy, X. Mary, and S. Canu, Non-parametric regression with wavelet kernels, Applied Stochastic Models in Business and Industry, vol.90, issue.2, pp.153-163, 2005.
DOI : 10.1002/asmb.533

G. Kimeldorf and G. Wahba, Some results on Tchebycheffian spline functions, Journal of Mathematical Analysis and Applications, vol.33, issue.1, pp.82-95, 1971.
DOI : 10.1016/0022-247X(71)90184-3

B. Schölkopf, R. Herbrich, and R. Williamson, A Generalized Representer Theorem, NeuroCOLT, 2000.
DOI : 10.1007/3-540-44581-1_27

B. Boashash and E. , Time-Frequency Signal Analysis: Methods and Applications., Biometrics, vol.49, issue.4, 2003.
DOI : 10.2307/2532288

P. Flandrin, Time-Frequency/Time-Scale Analysis, 1998.

N. Marinovitch, The singular value decomposition of the wigner distribution and its applications, " in The Wigner distribution: theory and applications in signal processing, 1997.

E. Bernat, W. Williams, and W. Gehring, Decomposing ERP time???frequency energy using PCA, Clinical Neurophysiology, vol.116, issue.6, p.13141334, 2005.
DOI : 10.1016/j.clinph.2005.01.019

Z. H. Mamar, P. Chainais, and A. Aussem, Probabilistic classifiers and time-scale representations: application to the monitoring of a tramway guiding system, Proc. European Symposium on Artificial Neural Networks, 2006.

C. Richard and R. Lengellé, Joint recursive implementation of time???frequency representations and their modified version by the reassignment method, Signal Processing, vol.60, issue.2, pp.163-179, 1997.
DOI : 10.1016/S0165-1684(97)80004-7

E. Chassande-mottin and A. Pai, Discrete time and frequency Wigner-Ville distribution: Moyal's formula and aliasing, IEEE Signal Processing Letters, vol.12, issue.7, pp.508-511, 2005.
DOI : 10.1109/LSP.2005.849493

F. Abdallah, C. Richard, and R. Lengellé, An Improved Training Algorithm for Nonlinear Kernel Discriminants, IEEE Transactions on Signal Processing, vol.52, issue.10, pp.2798-2806, 2004.
DOI : 10.1109/TSP.2004.834346

B. Schölkopf, R. Williamson, A. Smola, J. Shawe-taylor, and J. Platt, Support vector method for novelty detection, Proc. Advances in Neural Information Processing Systems, pp.582-588, 2000.

J. Vert, K. Tsuda, and B. Schölkopf, A primer on kernel methods, pp.35-70, 2004.

F. Bach and M. Jordan, Kernel independent component analysis, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)., pp.1-48, 2002.
DOI : 10.1109/ICASSP.2003.1202783

Y. Engel, S. Mannor, and R. Meir, The Kernel Recursive Least-Squares Algorithm, IEEE Transactions on Signal Processing, vol.52, issue.8, pp.2275-2285, 2004.
DOI : 10.1109/TSP.2004.830985

P. Honeiné, C. Richard, P. Flandrin, and J. Pothin, Optimal Selection of Time-Frequency Representations for Signal Classification: a Kernel-Target Alignment Approach, 2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, 2006.
DOI : 10.1109/ICASSP.2006.1660694