Transcription of Gaussian Processes for Machine Learning
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Gaussian Processes for Machine Learning
C. E. Rasmussen & C. K. I. Williams, Gaussian Processes for Machine Learning , the MIT Press, 2006,ISBN 2006 Massachusetts Institute of 2 RegressionSupervised 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 distri
using decision theory to make point predictions in an optimal way. A practical comparative example involving the learning of the inverse dynamics of a robot arm is presented in section 2.5. We give some theoretical analysis of Gaussian process regression in section …
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