Learning Gaussian Processes from Multiple Tasks - Yu, Kai; Tresp, Volker; Schwaighofer, Anton (Machine Learning: Proceedings of the 22nd International Conference (ICML 2005), 2005)
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.