A. Ganapathiraju, J. Hamaker, and J. Picone, Applications of Support Vector Machines to Speech Recognition, IEEE Transactions on Signal Processing, vol.52, issue.8, pp.2348-2355, 2004.
DOI : 10.1109/TSP.2004.831018

J. D. Millán, On the need for on-line learning in brain-computer interfaces, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541), 2004.
DOI : 10.1109/IJCNN.2004.1381116

A. Kampouraki, G. Manis, and C. Nikou, Heartbeat Time Series Classification With Support Vector Machines, IEEE Transactions on Information Technology in Biomedicine, vol.13, issue.4, pp.512-518, 2009.
DOI : 10.1109/TITB.2008.2003323

B. Blankertz, The BCI Competition 2003: Progress and Perspectives in Detection and Discrimination of EEG Single Trials, IEEE Transactions on Biomedical Engineering, vol.51, issue.6, pp.1044-1051, 2004.
DOI : 10.1109/TBME.2004.826692

B. Asl, S. Setarehdan, and M. Mohebbi, Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal, Artificial Intelligence in Medicine, vol.44, issue.1, pp.51-64, 2008.
DOI : 10.1016/j.artmed.2008.04.007

S. Lenser and M. Veloso, Non-Parametric Time Series Classification, Proceedings of the 2005 IEEE International Conference on Robotics and Automation, pp.3918-3923, 2006.
DOI : 10.1109/ROBOT.2005.1570719

O. Cappé, E. Moulines, and T. Rydèn, Inference in Hidden Markov Models, 2005.

Y. Altun, I. Tsochantaridis, and T. Hofmann, Hidden Markov support vector machines, International Conference in Machine Learning, p.3, 2003.

A. Sloin and D. Burshtein, Support Vector Machine Training for Improved Hidden Markov Modeling, IEEE Transactions on Signal Processing, vol.56, issue.1, p.172, 2008.
DOI : 10.1109/TSP.2007.906741

J. Bloit and X. Rodet, Short-time Viterbi for online HMM decoding: Evaluation on a real-time phone recognition task, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008.
DOI : 10.1109/ICASSP.2008.4518061
URL : https://hal.archives-ouvertes.fr/hal-01161222

A. Bordes, N. Usunier, and L. Bottou, Sequence Labelling SVMs Trained in One Pass, Machine Learning and Knowledge Discovery in Databases: ECML PKDD, pp.146-161, 2008.
DOI : 10.1007/978-3-540-87479-9_28
URL : https://hal.archives-ouvertes.fr/hal-00752369

T. Pistohl, T. Ball, A. Schulze-bonhage, A. Aertsen, and C. Mehring, Prediction of arm movement trajectories from ECoG-recordings in humans, Journal of Neuroscience Methods, vol.167, issue.1, pp.105-114, 2008.
DOI : 10.1016/j.jneumeth.2007.10.001

S. Salenius, R. Salmelin, C. Neuper, G. Pfurtscheller, and R. Hari, Human cortical 40 Hz rhythm is closely related to EMG rhythmicity, Neuroscience Letters, vol.213, issue.2, pp.75-78, 1996.
DOI : 10.1016/0304-3940(96)12796-8

X. Wang, T. Wang, and J. Bu, Color image segmentation using pixel wise support vector machine classification, Pattern Recognition, vol.44, issue.4, pp.777-787, 2011.
DOI : 10.1016/j.patcog.2010.08.008

D. Chai, H. Lin, and Q. Peng, Bisection approach for pixel labelling problem, Pattern Recognition, vol.43, issue.5, pp.1826-1834, 2010.
DOI : 10.1016/j.patcog.2009.11.002

D. Puig and M. Garcia, Automatic texture feature selection for image pixel classification, Pattern Recognition, vol.39, issue.11, 1996.
DOI : 10.1016/j.patcog.2006.05.016

F. Pacifici, M. Chini, and W. Emery, A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification, Remote Sensing of Environment, vol.113, issue.6, pp.1276-1292, 2009.
DOI : 10.1016/j.rse.2009.02.014

J. Benediktsson, J. A. Palmason, and J. R. Sveinsson, Classification of hyperspectral data from urban areas based on extended morphological profiles, IEEE Transactions on Geoscience and Remote Sensing, vol.43, issue.3, pp.480-490, 2005.
DOI : 10.1109/TGRS.2004.842478

M. Fauvel, J. A. Benediktsson, J. Chanussot, and J. R. Sveinsson, Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Morphological Profiles, IEEE Transactions on Geoscience and Remote Sensing, vol.46, issue.11, pp.3804-3814, 2008.
DOI : 10.1109/TGRS.2008.922034
URL : https://hal.archives-ouvertes.fr/hal-00283769

D. Tuia, F. Pacifici, M. Kanevski, and W. Emery, Classification of Very High Spatial Resolution Imagery Using Mathematical Morphology and Support Vector Machines, IEEE Transactions on Geoscience and Remote Sensing, vol.47, issue.11, pp.3866-3879, 2009.
DOI : 10.1109/TGRS.2009.2027895

Y. Lecun and Y. Bengio, Convolutional networks for images, speech, and time series The handbook of brain theory and neural networks, pp.255-258, 1995.

B. De-vries and J. C. Principe, The gamma model???A new neural model for temporal processing, Neural Networks, vol.5, issue.4, pp.565-576, 1992.
DOI : 10.1016/S0893-6080(05)80035-8

S. Lawrence and A. C. Tsoi, The Gamma MLP for speech phoneme recognition, IEEE Workshop on Neural Networks for Signal Processing VII, pp.785-791, 1996.

R. Flamary, B. Labbé, and A. Rakotomamonjy, Large margin filtering for Signal Sequence Labeling, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010.
DOI : 10.1109/ICASSP.2010.5495281
URL : https://hal.archives-ouvertes.fr/hal-00497300

J. C. Principe, B. De-vries, and P. G. De-oliveira, The gamma-filter-a new class of adaptive IIR filters with restricted feedback, IEEE Transactions on Signal Processing, vol.41, issue.2, pp.649-656, 1993.
DOI : 10.1109/78.193206

J. Shawe-taylor and N. Cristianini, Kernel methods for pattern analysis, 2004.
DOI : 10.1017/CBO9780511809682

O. Chapelle, Training a Support Vector Machine in the Primal, Neural Computation, vol.6, issue.5, pp.1155-1178, 2007.
DOI : 10.1198/106186005X25619

Y. Nesterov, Smooth minimization of non-smooth functions, Mathematical Programming, pp.127-152, 2005.
DOI : 10.1007/s10107-004-0552-5

P. Tseng, On accelerated proximal gradient methods for convexconcave optimization, submitted to SIAM Journal on Optimization, 2008.

X. Chen, W. Pan, J. Kwok, and J. Carbonell, Accelerated Gradient Method for Multi-task Sparse Learning Problem, 2009 Ninth IEEE International Conference on Data Mining, 2009.
DOI : 10.1109/ICDM.2009.128

J. Bonnans and A. Shapiro, Optimization problems with pertubation: A guided tour, SIAM Review, vol.40, issue.2, pp.202-227, 1998.

W. Hager and H. Zhang, A survey of nonlinear conjugate gradient methods, Pacific journal of Optimization, vol.2, issue.1, pp.35-58, 2006.

A. Rakotomamonjy, F. Bach, Y. Grandvalet, and S. Canu, SimpleMKL, Journal of Machine Learning Research, vol.9, pp.2491-2521, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00218338

M. Varma and B. Babu, More generality in efficient multiple kernel learning, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pp.1065-1072, 2009.
DOI : 10.1145/1553374.1553510

A. Beck, M. Teboulle, S. Shalev-shwartz, Y. Singer, and N. Srebro, A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems, Proceedings of the 24th international conference on Machine learning, pp.183-202, 2007.
DOI : 10.1137/080716542

D. Hunter and K. Lange, A Tutorial on MM Algorithms, The American Statistician, vol.58, issue.1, pp.30-38, 2004.
DOI : 10.1198/0003130042836

G. Dornhege, B. Blankertz, M. Krauledat, F. Losch, G. Curio et al., Optimizing spatio-temporal filters for improving braincomputer interfacing, Advances in Neural Information Processing Systems, p.315, 2006.

L. Gómez-chova, A. A. Nielsen, and G. Camps-valls, Explicit signal to noise ratio in reproducing kernel Hilbert spaces, 2011 IEEE International Geoscience and Remote Sensing Symposium, 2011.
DOI : 10.1109/IGARSS.2011.6049993

O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukerjhee, Choosing multiple parameters for SVM, Machine Learning, pp.131-159, 2002.

Y. Grandvalet and S. Canu, Adaptive scaling for feature selection in SVMs, Advances in Neural Information Processing Systems, 2003.

M. Christensen and A. Jakobsson, Optimal Filter Designs for Separating and Enhancing Periodic Signals, IEEE Transactions on Signal Processing, vol.58, issue.12, pp.5969-5983, 2010.
DOI : 10.1109/TSP.2010.2070497

M. Christensen, J. Hensen, A. Jakobsson, and S. Jensen, On Optimal Filter Designs for Fundamental Frequency Estimation, IEEE Signal Processing Letters, vol.15, pp.745-748, 2008.
DOI : 10.1109/LSP.2008.2003987

A. Bordes, S. Ertekin, J. Weston, and L. Bottou, Fast kernel classifiers with online and active learning, Journal of Machine Learning Research, vol.6, pp.1579-1619, 2005.
URL : https://hal.archives-ouvertes.fr/hal-00752361

O. Chapelle and A. Rakotomamonjy, Second order optimization of kernel parameters, NIPS Workshop on Automatic Selection of Optimal Kernels, 2008.

G. Golub and C. Van-loan, Matrix computations, 1996.