2004 | OriginalPaper | Buchkapitel
Gaussian Processes in Machine Learning
verfasst von : Carl Edward Rasmussen
Erschienen in: Advanced Lectures on Machine Learning
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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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.