Carl Edward, R and Hannes, N (2010) Gaussian processes for machine learning (GPML) toolbox. Journal of Machine Learning Research, 11. pp. 3011-3015. ISSN 1533-7928Full text not available from this repository.
The GPML toolbox provides a wide range of functionality for Gaussian process (GP) inference and prediction. GPs are specified by mean and covariance functions; we offer a library of simple mean and covariance functions and mechanisms to compose more complex ones. Several likelihood functions are supported including Gaussian and heavy-tailed for regression as well as others suitable for classification. Finally, a range of inference methods is provided, including exact and variational inference, Expectation Propagation, and Laplace’s method dealing with non-Gaussian likelihoods and FITC for dealing with large regression tasks.
|Additional Information:||Related urls = http://jmlr.csail.mit.edu/|
|Divisions:||Div F > Computational and Biological Learning|
|Depositing User:||Cron Job|
|Date Deposited:||28 Oct 2011 16:49|
|Last Modified:||13 Jan 2014 01:21|
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