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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© 2004 Springer-Verlag Berlin Heidelberg
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Rasmussen, C.E. (2004). Gaussian Processes in Machine Learning. In: Bousquet, O., von Luxburg, U., Rätsch, G. (eds) Advanced Lectures on Machine Learning. ML 2003. Lecture Notes in Computer Science(), vol 3176. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28650-9_4
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DOI: https://doi.org/10.1007/978-3-540-28650-9_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23122-6
Online ISBN: 978-3-540-28650-9
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