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.

icml05_multitask.pdf
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In  Machine Learning: Proceedings of the 22nd International Conference (ICML 2005)

Publisher  ACM

Details

TypeInproceedings
Pages1012–1019
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