Gaussian Processes for Machine Learning (GPML) Toolbox
2010
Article
ei
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.
Author(s): | Rasmussen, CE. and Nickisch, H. |
Journal: | Journal of Machine Learning Research |
Volume: | 11 |
Pages: | 3011-3015 |
Year: | 2010 |
Month: | November |
Day: | 0 |
Department(s): | Empirical Inference |
Bibtex Type: | Article (article) |
Digital: | 0 |
Language: | en |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
Links: |
Web
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BibTex @article{6779, title = {Gaussian Processes for Machine Learning (GPML) Toolbox}, author = {Rasmussen, CE. and Nickisch, H.}, journal = {Journal of Machine Learning Research}, volume = {11}, pages = {3011-3015}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, month = nov, year = {2010}, doi = {}, month_numeric = {11} } |