Gaussian processes in machine learning
CE Rasmussen - Summer school on machine learning, 2003 - Springer
Summer school on machine learning, 2003•Springer
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.
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.
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.
Springer
