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Gaussian Processes in Machine Learning

2004

Book Chapter

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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.

Author(s): Rasmussen, CE.
Volume: 3176
Pages: 63-71
Year: 2004
Day: 0

Series: Lecture Notes in Computer Science
Editors: Bousquet, O., U. von Luxburg and G. R{\"a}tsch
Publisher: Springer

Department(s): Empirical Inference
Bibtex Type: Book Chapter (inbook)

Address: Heidelberg
Note: Copyright by Springer
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
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BibTex

@inbook{2903,
  title = {Gaussian Processes in Machine Learning},
  author = {Rasmussen, CE.},
  volume = {3176},
  pages = {63-71},
  series = {Lecture Notes in Computer Science},
  editors = {Bousquet, O., U. von Luxburg and G. R{\"a}tsch},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Heidelberg},
  year = {2004},
  note = {Copyright by Springer}
}