@INPROCEEDINGS{YuTreSch05,
    author = "Yu, Kai and Tresp, Volker and Schwaighofer, Anton",
    editor = "De Raedt, Luc and Wrobel, Stefan",
    title = "Learning {G}aussian Processes from Multiple Tasks",
    booktitle = "Machine Learning: Proceedings of the 22nd International Conference (ICML 2005)",
    year = "2005",
    publisher = "Morgan Kaufman",
    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."
}