A. Argyriou, C. A. Micchelli, and M. Pontil, When is there a representer theorem? Vector vs matrix regularizers, J. of Machine Learning Res, vol.10, 2009.

M. Belkin and P. Niyogi, Semi-Supervised Learning on Riemannian Manifolds, Machine Learning, 2004.
DOI : 10.1023/B:MACH.0000033120.25363.1e

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

M. Belkin, P. Niyogi, and V. Sindhwani, Manifold regularization: A geometric framework for learning from labeled and unlabeled examples, Journal of Machine Learning Research, vol.7, pp.2399-2434, 2006.

A. Ben-hur and W. S. Noble, Kernel methods for predicting protein-protein interactions, Bioinformatics, vol.21, issue.Suppl 1, pp.38-46, 2005.
DOI : 10.1093/bioinformatics/bti1016

K. Bleakley, G. Biau, and J. Vert, Supervised reconstruction of biological networks with local models, Bioinformatics, vol.23, issue.13, pp.57-65, 2007.
DOI : 10.1093/bioinformatics/btm204

URL : https://hal.archives-ouvertes.fr/hal-00130277

A. Caponnetto, C. A. Micchelli, M. Pontil, Y. , and Y. , Universal multitask kernels, Journal of Machine Learning Research, vol.9, pp.1615-1646, 2008.

C. Cortes, M. Mohri, W. , and J. , A general regression technique for learning transductions, Proceedings of the 22nd international conference on Machine learning , ICML '05, pp.153-160, 2005.
DOI : 10.1145/1102351.1102371

P. Geurts, L. Wehenkel, and F. Buc, Kernelizing the output of tree-based methods, Proceedings of the 23rd international conference on Machine learning , ICML '06, 2006.
DOI : 10.1145/1143844.1143888

URL : https://hal.archives-ouvertes.fr/hal-00341946

P. Geurts, N. Touleimat, M. Dutreix, and F. Alché-buc, Inferring biological networks with output kernel trees, BMC Bioinformatics, vol.8, issue.Suppl 2, p.4, 2007.
DOI : 10.1186/1471-2105-8-S2-S4

URL : https://hal.archives-ouvertes.fr/hal-00341942

P. Geurts, L. Wehenkel, and F. Alché-buc, Gradient boosting for kernelized output spaces, Proceedings of the 24th international conference on Machine learning, ICML '07, 2007.
DOI : 10.1145/1273496.1273533

URL : https://hal.archives-ouvertes.fr/hal-00341945

A. Globerson, G. Chechik, F. Pereira, and N. Tishby, Euclidean embedding of co-occurrence data, Journal of Machine Learning Research, vol.8, pp.2265-2295, 2007.

M. A. Huynen, C. Von-mering, and P. Bork, Function prediction and protein networks, Current Opinion in Cell Biology, vol.15, issue.2, pp.191-198, 2003.
DOI : 10.1016/S0955-0674(03)00009-7

H. Kadri, E. Duflos, P. Preux, S. Canu, and M. Davy, Nonlinear functional regression: a functional rkhs approach, JMLR Proc. of Intl. Conf. on Artificial Intelligence and Statistics, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00510411

H. Kashima, T. Kato, Y. Yamanishi, M. Sugiyama, and K. Tsuda, Link propagation: A fast semisupervised learning algorithm for link prediction, Proc. of the 9th SIAM Intl. Conf. on Data Mining, pp.1099-1110, 2009.

T. Kato, K. Tsuda, and K. Asai, Selective integration of multiple biological data for supervised network inference, Bioinformatics, vol.21, issue.10, pp.2488-2495, 2005.
DOI : 10.1093/bioinformatics/bti339

R. I. Kondor and J. D. Lafferty, Diffusion kernels on graphs and other discrete input spaces, Proc. of the 19th Intl. Conf. on Machine Learning, 2002.

D. Liben-nowell and J. Kleinberg, The link-prediction problem for social networks, J. of the Am. Soc. for Information Science and Technology, issue.7, p.58, 2007.

C. A. Micchelli and M. A. Pontil, On learning vectorvalued functions, Neural Computation, vol.17, 2005.

K. Miller, T. Griffiths, J. , and M. , Nonparametric latent feature models for link prediction, Adv. in Neural Information Processing Systems 22, 2009.

E. Senkene and A. Tempel-'man, Hilbert spaces of operator-valued functions, Mathematical Transactions of the Academy of Sciences of the Lithuanian SSR, vol.12, issue.No. 4, pp.665-670, 1973.
DOI : 10.1007/BF01630739

B. Taskar, M. Wong, P. Abbeel, and D. Koller, Link prediction in relational data, Advances in Neural Information Processing Systems 15, 2003.

K. Tsuda, S. Akaho, and K. Asai, The em algorithm for kernel matrix completion with auxiliary data, J. of Machine Learning Research, vol.4, pp.67-81, 2003.

Y. Yamanishi, J. Vert, and M. Kanehisa, Protein network inference from multiple genomic data: a supervised approach, Bioinformatics, vol.20, issue.Suppl 1, 2004.
DOI : 10.1093/bioinformatics/bth910

URL : https://hal.archives-ouvertes.fr/hal-00433586

D. Zhou, O. Bousquet, T. N. Lal, J. Weston, and B. Schölkopf, Learning with local and global consistency, Advances in Neural Information Processing Systems 16, 2004.