When is there a representer theorem? Vector vs matrix regularizers, J. of Machine Learning Res, vol.10, 2009. ,
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=10.1.1.78.2757
Manifold regularization: A geometric framework for learning from labeled and unlabeled examples, Journal of Machine Learning Research, vol.7, pp.2399-2434, 2006. ,
Kernel methods for predicting protein-protein interactions, Bioinformatics, vol.21, issue.Suppl 1, pp.38-46, 2005. ,
DOI : 10.1093/bioinformatics/bti1016
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
Universal multitask kernels, Journal of Machine Learning Research, vol.9, pp.1615-1646, 2008. ,
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
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
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
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
Euclidean embedding of co-occurrence data, Journal of Machine Learning Research, vol.8, pp.2265-2295, 2007. ,
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
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
Link propagation: A fast semisupervised learning algorithm for link prediction, Proc. of the 9th SIAM Intl. Conf. on Data Mining, pp.1099-1110, 2009. ,
Selective integration of multiple biological data for supervised network inference, Bioinformatics, vol.21, issue.10, pp.2488-2495, 2005. ,
DOI : 10.1093/bioinformatics/bti339
Diffusion kernels on graphs and other discrete input spaces, Proc. of the 19th Intl. Conf. on Machine Learning, 2002. ,
The link-prediction problem for social networks, J. of the Am. Soc. for Information Science and Technology, issue.7, p.58, 2007. ,
On learning vectorvalued functions, Neural Computation, vol.17, 2005. ,
Nonparametric latent feature models for link prediction, Adv. in Neural Information Processing Systems 22, 2009. ,
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
Link prediction in relational data, Advances in Neural Information Processing Systems 15, 2003. ,
The em algorithm for kernel matrix completion with auxiliary data, J. of Machine Learning Research, vol.4, pp.67-81, 2003. ,
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
Learning with local and global consistency, Advances in Neural Information Processing Systems 16, 2004. ,