Documentation for GPML Matlab Code
The code provided here demonstrates the main algorithms from Rasmussen
and Williams: Gaussian Processes
for Machine Learning.
The code is written in Matlab®, and should work with version 6 and
version 7. Bug reports should be sent to the authors. All the code
including demonstrations and html documentation can be downloaded in a
tar
or zip
archive file. Previous versions of the code may be available here. Please
read the copyright notice.
After unpacking the tar or zip file you will find
3 subdirectories: gpml, gpml-demo and doc.
The directory gpml contains the basic functions for GP regression,
GP binary classification, and sparse approximate methods for GP regression.
The directory gpml-demo contains
Matlab® scripts with names "demo_*.m". These provide small
demonstrations of the various programs provided.
The directory doc contains four html files providing
documentation. This information can also be accessed via
the www at http://www.GaussianProcess.org/gpml/code.
The code should run directly as provided, but some demos require a lot of
computation. A significant speedup may be attained by compiling the mex
files, see the rudimentary instructions on how to do this in the README file.
The documentation is divided into three sections:
Regression
Basic Gaussian process regression (GPR)
code allowing flexible specification of the covariance function.
Binary Classification
Gaussian process classification (GPC)
demonstrates implementations of Laplace and EP approximation methods for binary
GP classification.
Sparse Approximation methods for Gaussian Process Regression
Approximation methods for GPR demonstrates the
methods of subset of datapoints (SD), subset of regressors (SR)
and projected process (PP) approximations.
Other Gaussian Process Code
A table of other sources of useful Gaussian process software, unrelated to the
book, may be found here. This
includes pointers a number of packages that can handle multi-class
classification, e.g. fbm (Radford Neal),
c++-ivm (Neil Lawrence), gpclass (David
Barber and Chris Williams), klr (kernel multiple
logistic regression, by Matthias Seeger), and
VBGP (Mark Girolami and Simon Rogers).
Go back to the web page for
Gaussian Processes for Machine Learning.
Last modified: Tue Jun 26 10:43:51 CET 2007