C. J. Burges, From RankNet to LambdaRank to LambdaMART : An overview, Learning, vol.11, pp.23-581, 2010.

M. K. Carroll, G. A. Cecchi, I. Rish, R. Garg, and A. R. Rao, Prediction and interpretation of distributed neural activity with sparse models, NeuroImage, vol.44, issue.1, pp.112-122, 2009.
DOI : 10.1016/j.neuroimage.2008.08.020

E. Cauvet, Traitement des Structures Syntaxiques dans le langage et dans la musique, 2012.

R. Cuingnet, C. Rosso, M. Chupin, S. Lehéricy, D. Dormont et al., Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis, vol.15, issue.5, 2011.
DOI : 10.1016/j.media.2011.05.007

URL : https://hal.archives-ouvertes.fr/hal-00795741

H. Drucker, C. J. Burges, L. Kaufman, A. J. Smola, and V. Vapnik, Support vector regression machines, pp.155-161, 1996.

J. V. Haxby, M. I. Gobbini, M. L. Furey, A. Ishai, J. L. Schouten et al., Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex, Science, vol.293, issue.5539, pp.2425-2430, 2001.
DOI : 10.1126/science.1063736

J. D. Haynes and G. Rees, Decoding mental states from brain activity in humans, Nature Reviews Neuroscience, vol.16, issue.7, p.523, 2006.
DOI : 10.1038/nrn1931

R. Herbrich, T. Graepel, and K. Obermayer, Large margin rank boundaries for ordinal regression, pp.115-132, 2000.

K. Jimura and R. A. Poldrack, Analyses of regional-average activation and multivoxel pattern information tell complementary stories, Neuropsychologia, vol.50, issue.4, pp.1-9, 2011.
DOI : 10.1016/j.neuropsychologia.2011.11.007

T. Joachims, Training linear SVMs in linear time, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '06, pp.217-226, 2006.
DOI : 10.1145/1150402.1150429

K. N. Kay, T. Naselaris, R. J. Prenger, and J. L. Gallant, Identifying natural images from human brain activity, Nature, vol.79, issue.7185, pp.352-355, 2008.
DOI : 10.1038/nature06713

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3556484

S. Laconte, S. Strother, V. Cherkassky, J. Anderson, and X. Hu, Support vector machines for temporal classification of block design fMRI data, NeuroImage, vol.26, issue.2, pp.317-329, 2005.
DOI : 10.1016/j.neuroimage.2005.01.048

H. Liu, M. Palatucci, and J. Zhang, Blockwise coordinate descent procedures for the multi-task lasso, with applications to neural semantic basis discovery, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pp.649-656, 2009.
DOI : 10.1145/1553374.1553458

V. Michel, A. Gramfort, G. Varoquaux, E. Eger, and B. Thirion, Total Variation Regularization for fMRI-Based Prediction of Behavior, IEEE Transactions on Medical Imaging, vol.30, issue.7, pp.1328-1340, 2011.
DOI : 10.1109/TMI.2011.2113378

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikit-learn: Machine Learning in Python, Journal of Machine Learning Research, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

M. Richardson, A. Prakash, and E. Brill, Beyond PageRank, Proceedings of the 15th international conference on World Wide Web , WWW '06, pp.707-715, 2006.
DOI : 10.1145/1135777.1135881

S. M. Tom, C. R. Fox, C. Trepel, and R. A. Poldrack, The Neural Basis of Loss Aversion in Decision-Making Under Risk, Science, vol.315, issue.5811, pp.515-518, 2007.
DOI : 10.1126/science.1134239