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  Gaussian Processes for Machine Learning (GPML) Toolbox

Rasmussen, C., & Nickisch, H. (2010). Gaussian Processes for Machine Learning (GPML) Toolbox. The Journal of Machine Learning Research, 11, 3011-3015.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BD60-4 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-68B4-E
Genre: Journal Article

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 Creators:
Rasmussen, CE, Author              
Nickisch, H1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: 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.

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 Dates: 2010-11
 Publication Status: Published in print
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 Rev. Method: -
 Identifiers: BibTex Citekey: 6779
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Title: The Journal of Machine Learning Research
Source Genre: Journal
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Publ. Info: Cambridge, MA : MIT Press
Pages: - Volume / Issue: 11 Sequence Number: - Start / End Page: 3011 - 3015 Identifier: ISSN: 1532-4435
CoNE: https://pure.mpg.de/cone/journals/resource/111002212682020_1