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

C. O. and M. E. Ryd-`-enryd-`-ryd-`-en, Inference in Hidden Markov Models, 2005.

C. O. Vapnik-v, . Bousquet-o, and . Mukerjhee-s, Choosing multiple parameters for SVM, Machine Learning, pp.131-159, 2002.

D. G. Blankertz-b, L. F. Krauledat-m, and C. G. Muller-k, Optimizing spatio-temporal filters for improving brain-computer interfacing, Advances in Neural Information Processing Systems, p.315, 2006.

F. R. and L. B. Rakotomamonjy-a, Large margin filtering for signal sequence labeling, International Conference on Acoustic, Speech and Signal Processing, 2010.

G. A. and H. J. Picone-j, Applications of support vector machines to speech recognition, IEEE Transactions on Signal Processing, issue.8, pp.52-2348, 2004.

G. Y. Canu-s, Adaptive scaling for feature selection in svms, Advances in Neural Information Processing Systems, 2003.

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

H. D. Lange-k, A Tutorial on MM Algorithms, The American Statistician, vol.58, issue.1, pp.30-38, 2004.

L. H. and L. C. Weng-r, A note on Platt's probabilistic outputs for support vector machines, Machine Learning, pp.267-276, 2007.

M. Mill´an and J. D. , On the need for on-line learning in brain-computer interfaces, Proc. Int. Joint Conf. on Neural Networks, 2004.

P. T. Ball-t, A. A. Schulze-bonhage, and . Mehring-c, Prediction of arm movement trajectories from ecog-recordings in humans, Journal of Neuroscience Methods, vol.167, issue.1, pp.105-114, 2008.

S. S. Salmelin-r, P. G. Neuper-c, and . R. Hari, Human cortical 40 Hz rhythm is closely related to EMG rhythmicity, Neuroscience letters, vol.213, issue.2, pp.75-78, 1996.

S. A. Burshtein-d, Support vector machine training for improved hidden Markov modeling, IEEE Transactions on Signal Processing, vol.56, issue.1, p.172, 2008.