GaussianProcesses (original) (raw)

Implements Gaussian Processes for regression without hyperparameter-tuning. For more information see

David J.C. Mackay (1998). Introduction to Gaussian Processes. Dept. of Physics, Cambridge University, UK.

BibTeX:

@misc{Mackay1998, address = {Dept. of Physics, Cambridge University, UK}, author = {David J.C. Mackay}, title = {Introduction to Gaussian Processes}, year = {1998}, PS = {http://wol.ra.phy.cam.ac.uk/mackay/gpB.ps.gz} }

Valid options are:

-D If set, classifier is run in debug mode and may output additional info to the console

-L Level of Gaussian Noise. (default: 1.0)

-N Whether to 0=normalize/1=standardize/2=neither. (default: 0=normalize)

-K The Kernel to use. (default: weka.classifiers.functions.supportVector.PolyKernel)

Options specific to kernel weka.classifiers.functions.supportVector.RBFKernel:

-D Enables debugging output (if available) to be printed. (default: off)

-no-checks Turns off all checks - use with caution! (default: checks on)

-C The size of the cache (a prime number), 0 for full cache and -1 to turn it off. (default: 250007)

-G The Gamma parameter. (default: 0.01)