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. © Springer-Verlag 2004.
CITATION STYLE
Rasmussen, C. E. (2004). Gaussian Processes in machine learning. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3176, 63–71. https://doi.org/10.1007/978-3-540-28650-9_4
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