Gaussian Processes in Machine Learning
Carl Edward Rasmussen · Lecture notes in computer science · 2004
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. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.