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ESANN
2008

Approximation of Gaussian process regression models after training

13 years 5 months ago
Approximation of Gaussian process regression models after training
The evaluation of a standard Gaussian process regression model takes time linear in the number of training data points. In this paper, the models are approximated in the feature space after training. It is empirically shown that the time required for evaluation can be drastically reduced without considerable loss in performance.
Thorsten Suttorp, Christian Igel
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where ESANN
Authors Thorsten Suttorp, Christian Igel
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