Gaussian Processes for Machine Learning

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C. E. Rasmussen & C. K. I. Williams, Gaussian Processes for Machine Learning , the MIT Press, 2006,ISBN 2006 Massachusetts Institute of 2RegressionSupervised Learning can be divided into regression and classification the outputs for classification are discrete class labels, regression isconcerned with the prediction of continuous quantities. For example, in a fi-nancial application, one may attempt to predict the price of a commodity asa function of interest rates, currency exchange rates, availability and this chapter we describe Gaussian process methods for regression problems;classification problems are discussed in chapter are several ways to interpret Gaussian process (GP) regression can think of a Gaussian process as defining a distribution over functions,and inference taking place directly in the space of functions, thefunction-spacetwo equivalent viewsview.

C. E. Rasmussen & C. K. I. Williams, Gaussian Processes for Machine Learning, the MIT Press, 2006, ISBN 026218253X. 2006 Massachusetts Institute of Technology.c www.GaussianProcess.org/gpml

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