A. A. and E. T. Pontil-m, Convex multi-task feature learning, Machine Learning, 2008.

B. J. Xiong-t, Y. S. , and D. M. Rao-b, An improved multi-task learning approach with applications in medical diagnosis, Proceedings of the 18th European Conference on Machine Learning, 2008.

C. E. and W. M. Boyd-s, Enhancing sparsity by reweighted ? 1 minimization, J. Fourier Analysis and Applications, vol.14, pp.877-905, 2008.

C. S. Rao-b and E. K. Kreutz-delgado-k, Sparse solutions to linear inverse problems with multiple measurement vectors, IEEE Transactions on Signal Processing, vol.53, issue.7, pp.2477-2488, 2005.

E. T. Pontil-m, Regularized multi-task learning, Proceedings of the tenth Conference on Knowledge Discovery andData minig, 2004.

H. R. Thoai-n, Dc programming : overview, Journal of Optimization Theory and Applications, vol.103, pp.1-41, 1999.

K. T. Kashima-h and S. M. Asai-k, Multi-task learning via conic programming, Advances in Neural Information Processing Systems, 2008.

K. K. Fu-w, Asymptotics for lasso-type estimators, The Annals of Statistics, vol.28, issue.5, pp.1356-1378, 2000.
DOI : 10.1214/aos/1015957397

L. H. and L. J. Wasserman-l, Non parametric regression and classification with joint sparsity constraints, Advances in Neural Information Processing Systems 21, 2009.

O. G. and T. B. Jordan-m, Multi-task feature selection, 2007.

Q. A. and C. M. Darrell, Transfer learning for image classification with sparse prototype representations, Proceedings of CVPR, pp.2491-2521, 2008.

R. A. Guigue-v and M. G. Alvarado-v, Ensemble of SVMs for improving brain-computer interface p300 speller performances, 15th International Conference on Artificial Neural Networks, 2005.

S. V. and N. P. Belkin-m, Beyond the point cloud : from transductive to semi-supervised learning, Proceedings of International Conference on Machine Learning, 2005.

S. B. Torres-d and . Lanckriet-g, Sparse eigen methods by d.c. programming, Proceedings of the 24 th International Conference on Machine Learning, 2007.

S. M. and G. Y. Rakotomamonjy-a, Composite kernel learning, Proceedings of the 22nd International Conference on Machine Learning, 2008.

X. T. Bi, . Rao-b, and . Cherkassky-v, Probabilistic joint feature selection for multi-task learning, Proceedings of SIAM International Conference on Data Mining, 2006.

Y. K. and T. V. Schwaighofer-a, Learning gaussian processes from multiple tasks, Proceeding of the 22nd International Conference on Machine Learning, 2005.

Z. P. and R. G. Yu-b, The composite absolute penalties family for grouped and hierarchical variable selection, Annals of Statistics