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: |
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PostScript |
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}, doi = {} } |