Learning Gaussian Processes from Multiple Tasks
Kai Yu, Volker Tresp and Anton Schwaighofer
In: International Conference on Machine Learning ICML 2005, Aug 2005, Bonn, Germany.
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.