Chapter

Advanced Lectures on Machine Learning

Volume 3176 of the series Lecture Notes in Computer Science pp 63-71

Gaussian Processes in Machine Learning

  • Carl Edward RasmussenAffiliated withMax Planck Institute for Biological Cybernetics

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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.