Sciweavers

Share
ICML
2005
IEEE

Learning Gaussian processes from multiple tasks

9 years 11 months ago
Learning Gaussian processes from multiple tasks
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.
Kai Yu, Volker Tresp, Anton Schwaighofer
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2005
Where ICML
Authors Kai Yu, Volker Tresp, Anton Schwaighofer
Comments (0)
books