GPR

Gaussian Process regression

      W = GPR(A,KERNEL,S_noise)

Input
A	Dataset
KERNEL	Untrained mapping to compute kernel by A*(A*KERNEL) during training, or B*(A*KERNEL) during evaluation with  dataset B S_noise Standard deviation of the noise

Output
W	Mapping: Gaussian Process regression

Description

Fit a Gaussian Process regressor on dataset A. For a nonlinear regressor,  define kernel mapping KERNEL. For kernel definitions, have a look at  proxm.m.

See also

svmr, proxm, linearr, testr, plotr,