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Gaussian Processes in Machine Learning

by: Carl Rasmussen

edited by: Olivier Bousquet, Ulrike von Luxburg, Gunnar Rätsch

Advanced Lectures on Machine Learning In Advanced Lectures on Machine Learning, Vol. 3176 (2004), pp. 63-71, doi:10.1007/978-3-540-28650-9_4  Key: citeulike:3154204

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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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