1.5. Gaussian Processes 1.5.1. Examples 1.5.1.1. An introductory regression example 1.5.1.2. Fitting Noisy Data XIAO LIYING.

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Presentation transcript:

1.5. Gaussian Processes Examples An introductory regression example Fitting Noisy Data XIAO LIYING

1.5. Gaussian Processes Gaussian Processes for Machine Learning (GPML) is a generic supervised learning method primarily designed to solve regression problems. The advantages of Gaussian Processes for Machine Learning are: 1The prediction interpolates the observations (at least for regular correlation models). 2 The prediction is probabilistic (Gaussian) so that one can compute empirical confidence intervals and exceedance probabilities. 3 Versatile: different linear regression models and correlation models can be specified.linear regression models correlation models

The disadvantages of Gaussian Processes for Machine Learning include: 1It is not sparse. 2It loses efficiency in high dimensional spaces – namely when the number of features exceeds a few dozens. 3Classification is only a post-processing.

An introductory regression example The function g(x)=xsin(x). the function is evaluated onto a design of experiments.

Fitting Noisy Data