📄 index.html
字号:
<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"><html> <head> <title>Documentation for GPML Matlab Code</title> <link type="text/css" rel="stylesheet" href="style.css"> </head> <body><h2>Documentation for GPML Matlab Code</h2>The code provided here demonstrates the main algorithms from Rasmussenand Williams: <a href="http://www.gaussianprocess.org/gpml">Gaussian Processesfor Machine Learning</a>.</p>The code is written in Matlab®, and should work with version 6 andversion 7. Bug reports should be sent to the authors. All the codeincluding demonstrations and html documentation can be downloaded in a<ahref="http://www.gaussianprocess.org/gpml/code/gpml-matlab.tar.gz">tar</a>or <ahref="http://www.gaussianprocess.org/gpml/code/gpml-matlab.zip">zip</a>archive file. Previous versions of the code may be available <ahref="http://www.gaussianprocess.org/gpml/code/old">here</a>. Pleaseread the <a href="../gpml/Copyright">copyright</a> notice.</p>After unpacking the tar or zip file you will find 3 subdirectories: gpml, gpml-demo and doc.</p>The directory gpml contains the basic functions for GP regression,GP binary classification, and sparse approximate methods for GP regression.</p>The directory gpml-demo contains Matlab® scripts with names "demo_*.m". These provide smalldemonstrations of the various programs provided. </p>The directory doc contains four html files providing documentation. This information can also be accessed viathe www at <a href="http://www.GaussianProcess.org/gpml/code">http://www.GaussianProcess.org/gpml/code</a>.</p>The code should run directly as provided, but some demos require a lot ofcomputation. A significant speedup may be attained by compiling the mexfiles, see the rudimentary instructions on how to do this in the <ahref="../README">README</a> file.</p>The documentation is divided into three sections:<h3>Regression</h3>Basic <a href="regression.html">Gaussian process regression</a> (GPR)code allowing flexible specification of the covariance function.<h3>Binary Classification</h3><a href="classification.html">Gaussian process classification</a> (GPC)demonstrates implementations of Laplace and EP approximation methods for binaryGP classification.<h3>Sparse Approximation methods for Gaussian Process Regression</h3><a href="sparse-approx.html">Approximation methods for GPR</a> demonstrates themethods of <b>subset of datapoints</b> (SD), <b>subset of regressors</b> (SR)and <b>projected process</b> (PP) approximations.<br><br><br><h3>Other Gaussian Process Code</h3>A table of other sources of useful Gaussian process software, unrelated to the<a href="http://www.gaussianprocess.org/gpml">book</a>, may be found <ahref="http://www.gaussianprocess.org/#code">here<a>. This includes pointers a number of packages that can handle multi-classclassification, e.g. <tt>fbm</tt> (Radford Neal), <tt>c++-ivm</tt> (Neil Lawrence), <tt>gpclass</tt> (DavidBarber and Chris Williams), <tt>klr</tt> (kernel multiplelogistic regression, by Matthias Seeger), and <tt>VBGP</tt> (Mark Girolami and Simon Rogers).</p><br><br><br>Go back to the <a href="http://www.gaussianprocess.org/gpml">web page</a> forGaussian Processes for Machine Learning.<hr><!-- Created: Fri Oct 14 12:23:09 CEST 2005 --><!-- hhmts start -->Last modified: Tue Jun 26 10:43:51 CET 2007<!-- hhmts end --> </body></html>⌨️ 快捷键说明
复制代码
Ctrl + C
搜索代码
Ctrl + F
全屏模式
F11
切换主题
Ctrl + Shift + D
显示快捷键
?
增大字号
Ctrl + =
减小字号
Ctrl + -