@inproceedings{yu2005learning,
author = {Yu, Kai and Tresp, Volker and Schwaighofer, Anton},
title = {Learning Gaussian Processes from Multiple Tasks},
booktitle = {Machine Learning: Proceedings of the 22nd International Conference (ICML 2005)},
year = {2005},
month = {January},
abstract = {We consider the problem of multi-task learning, that is, learning multiple related functions. Our approach is based on a hierarchical Bayesian framework, that exploits the equivalence between parametric linear models and nonparametric Gaussian processes (GPs). The resulting models can be learned easily via an EM-algorithm. Empirical studies on multi-label text categorization suggest that the presented models allow accurate solutions of these multi-task problems.},
publisher = {ACM},
url = {http://approjects.co.za/?big=en-us/research/publication/learning-gaussian-processes-from-multiple-tasks/},
pages = {1012-1019},
edition = {Machine Learning: Proceedings of the 22nd International Conference (ICML 2005)},
}