PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

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.

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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
Information Retrieval & Textual Information Access
ID Code:1309
Deposited By:Anton Schwaighofer
Deposited On:28 November 2005