Abstract
We give a basic introduction to Gaussian Process regression models. We focus on understanding the role of the stochastic process and how it is used to define a distribution over functions. We present the simple equations for incorporating training data and examine how to learn the hyperparameters using the marginal likelihood. We explain the practical advantages of Gaussian Process and end with conclusions and a look at the current trends in GP work.
Keywords
Machine learningArtificial intelligenceComputer scienceOnline machine learningGaussian processProbabilistic logicRelevance vector machineSupport vector machineKernel methodArtificial neural networkGaussian
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Publication Info
- Year
- 2005
- Type
- book
- Citations
- 10408
- Access
- Closed
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Cite This
Carl Edward Rasmussen,
Christopher K. I. Williams
(2005).
Gaussian Processes for Machine Learning.
The MIT Press eBooks
.
https://doi.org/10.7551/mitpress/3206.001.0001
Identifiers
- DOI
- 10.7551/mitpress/3206.001.0001