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  Gaussian Processes in Machine Learning

Rasmussen, C. (2004). Gaussian Processes in Machine Learning. In O. Bousquet, U. von Luxburg, & G. Rätsch (Eds.), Advanced Lectures on Machine Learning: ML Summer Schools 2003, Canberra, Australia, February 2 - 14, 2003, Tübingen, Germany, August 4 - 16, 2003 (pp. 63-71). Berlin, Germany: Springer.

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 Creators:
Rasmussen, CE1, 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: 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.

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 Dates: 2004-09
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 2903
DOI: 10.1007/978-3-540-28650-9_4
 Degree: -

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Title: ML Summer Schools 2003
Place of Event: Tübingen, Germany
Start-/End Date: 2003-08-04 - 2003-08-16

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Title: Advanced Lectures on Machine Learning: ML Summer Schools 2003, Canberra, Australia, February 2 - 14, 2003, Tübingen, Germany, August 4 - 16, 2003
Source Genre: Proceedings
 Creator(s):
Bousquet, O, Editor           
von Luxburg, U1, Editor           
Rätsch, G2, Editor           
Affiliations:
1 Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794            
2 Friedrich Miescher Laboratory, Max Planck Society, ou_2575692            
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 63 - 71 Identifier: ISBN: 978-3-540-23122-6