F. Bach, G. Lanckriet, and M. Jordan, Multiple kernel learning, conic duality, and the SMO algorithm, Twenty-first international conference on Machine learning , ICML '04, pp.41-48, 2004.
DOI : 10.1145/1015330.1015424

M. Balcan, A. Blum, and K. Yang, Co-training and expansion: Towards bridging theory and practice, NIPS, 2004.

A. B. Blum and T. M. Mitchell, Combining labeled and unlabeled data with co-training, Proceedings of the eleventh annual conference on Computational learning theory , COLT' 98, pp.92-100, 1998.
DOI : 10.1145/279943.279962

U. Brefeld, T. Gärtner, T. Scheffer, and S. Wrobel, Efficient co-regularised least squares regression, Proceedings of the 23rd international conference on Machine learning , ICML '06, pp.137-144, 2006.
DOI : 10.1145/1143844.1143862

C. Brouard, F. D-'alche-buc, and M. Szafranski, Semi-supervised penalized output kernel regression for link prediction, ICML, pp.593-600, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00654123

G. Cavallanti, N. Cesa-bianchi, and C. Gentile, Linear algorithms for online multitask classification, Omnipress Proceedings of the 21st Annual Conference on Learning Theory, 2008.

N. Cesa-bianchi, D. R. Hardoon, and G. Leen, Guest Editorial: Learning from multiple sources, Machine Learning, pp.1-3, 2010.
DOI : 10.1007/s10994-010-5169-8

M. Christoudias, R. Urtasun, and T. Darrell, Multi-view learning in the presence of view disagreement, UAI, 2008.

K. Crammer, M. Kearns, and J. Wortman, Learning from multiple sources, Journal of Machine Learning Research, vol.9, pp.1757-1774, 2008.

T. Evgeniou, C. Micchelli, and M. Pontil, Learning multiple tasks with kernel methods, Journal of Machine Learning Research, vol.6, pp.615-637, 2005.

J. D. Farquhar, D. R. Hardoon, H. Meng, J. Shawe-taylor, and S. Szedmak, Two view learning: SVM-2K, theory and practice, NIPS, pp.355-362, 2006.

K. Fukumizu, F. R. Bach, and M. I. Jordan, Dimensionality reduction for supervised learning with reproducing kernel Hilbert spaces, Journal of Machine Learning Research, vol.5, pp.73-99, 2004.

A. Gretton, O. Bousquet, A. Smola, and B. Scholkopf, Measuring Statistical Dependence with Hilbert-Schmidt Norms, Algorithmic Learning Theory: 16th International Conference, 2005.
DOI : 10.1007/11564089_7

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.105.477

H. Kadri, A. Rabaoui, P. Preux, E. Duflos, and A. Rakotomamonjy, Functional regularized least squares classification with operator-valued kernels, ICML, pp.993-1000, 2011.

H. Kadri, S. Ayache, C. Caponni, S. Koço, F. X. Dupé et al., The multi-task learning view of multimodal data, CNRS, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01070601

H. Kadri, M. Ghavamzadeh, and P. Preux, A generalized kernel approach to structured output learning, ICML, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00695631

J. Kludas, E. Bruno, and S. Marchand-maillet, Information Fusion in Multimedia Information Retrieval, Adaptive Multimedia Retrieval, pp.147-159, 2007.
DOI : 10.1007/978-3-540-79860-6_12

A. Kumar, H. Daumé, and I. , A co-training approach for multi-view spectral clustering, ICML, pp.393-400, 2011.

A. Kumar, P. Rai, H. Daumé, and I. , Co-regularized multi-view spectral clustering, NIPS, Granada, 2011.

C. H. Lampert, H. Nickisch, and S. Harmeling, Learning to detect unseen object classes by between-class attribute transfer, 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009.
DOI : 10.1109/CVPR.2009.5206594

Y. Luo, D. Tao, C. Xu, D. Li, and C. Xu, Vector-valued multi-view semi-supervised learning for multi-label image classification, Twenty-Seventh AAAI Conference on Artificial Intelligence, 2013.

C. Micchelli and M. Pontil, Kernels for multi?task learning

C. Micchelli and M. Pontil, On Learning Vector-Valued Functions, Neural Computation, vol.1, issue.1, pp.177-204, 2005.
DOI : 10.1109/34.735807

H. Q. Minh, L. Bazzani, and V. Murino, A unifying framework for vector-valued manifold regularization and multi-view learning, ICML, 2013.

I. Muslea and C. A. Knoblock, Active learning with multiple views, J. Artif. Intell. Res. (JAIR), vol.27, pp.203-233, 2006.

T. Pahikkala, H. Suominen, and J. Boberg, Efficient cross-validation for kernelized leastsquares regression with sparse basis expansions, Machine Learning, pp.381-407, 2012.

M. Reisert and H. Burkhardt, Learning equivariant functions with matrix valued kernels, Journal of Machine Learning Research, vol.8, pp.385-408, 2007.

R. Rifkin and R. A. Lippert, Notes on regularized least-squares, 2007.

R. Rifkin, G. Yeo, and T. Poggio, Regularized least squares classification Advances in Learning Theory: Methods, Model and Applications NATO Science Series III: Computer and Systems Sciences, pp.131-153, 2003.

B. Schölkopf and A. J. Smola, Learning with kernels: Support vector machines, regularization , optimization, and beyond, 2001.

L. Schwartz, Sous-espaces hilbertiens d'espaces vectoriels topologiques et noyaux associés (noyaux reproduisants) Journal d'Analyse Mathématique, pp.115-256, 1964.
DOI : 10.1007/bf02786620

V. Sindhwani and D. S. Rosenberg, An RKHS for multi-view learning and manifold coregularization, ICML, pp.976-983, 2008.

S. Szedmak, J. Shawe-taylor, and E. Parado-hernandez, Learning via linear operators: Maximum margin regression, 2005.

G. Wahba, Spline models for observational data, Society for Industrial and Applied Mathematics(SIAM ), 1990.
DOI : 10.1137/1.9781611970128

G. Wahba, Multivariate function and operator estimation, based on smoothing splines and reproducing kernels, Nonlinear Modeling and Forecasting of Proc. of the Santa Fe Institute, pp.95-112, 1992.

W. Wang and Z. Zhou, On multi-view active learning and the combination with semisupervised learning, ICML, pp.1152-1159, 2008.