PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Generalization Errors and Learning Curves for Regression with Multi-task Gaussian Processes
Kian Ming Chai
Advances in Neural Information Processing Systems Volume 22, pp. 279-287, 2009.

Abstract

We provide some insights into how task correlations in multi-task Gaussian process (GP) regression affect the generalization error and the learning curve. We analyze the asymmetric two-tasks case, where a secondary task is to help the learning of a primary task. Within this setting, we give bounds on the generalization error and the learning curve of the primary task. Our approach admits intuitive understandings of the multi-task GP by relating it to single-task GPs. For the case of one-dimensional input-space under optimal sampling with data only for the secondary task, the limitations of multi-task GP can be quantified explicitly.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:5576
Deposited By:Kian Ming Chai
Deposited On:08 March 2010