SVM Toolbox

MATLAB Support Vector Machine Toolbox. This is a beta version of a MATLAB toolbox implementing Vapnik’s support vector machine, as described in [1]. Training is performed using the SMO algorithm, due to Platt [2], implemented as a mex file (for speed). Before you use the toolbox you need to run the compilemex script to recompile them (if there are problems running this script, make sure you have the mex compiler set up correctly - you may need to see your sys-admin to do this). At the moment this is the only documentation for the toolbox but the file demo.m provides a simple demonstration that ought to be enough to get started. For a good introduction to support vector machines, see the excellent book by Cristianini and Shawe-Taylor [3]. Key features of this toolbox: C++ MEX implementation of the SMO training algorithm, with caching of kernel evaluations for efficiency. Support for multi-class support vector classification using max wins, pairwise [4] and DAG-SVM [5] algorithms. A model selection criterion (the xi-alpha bound [6,7] on the leave-one-out cross-validation error). Object oriented design, currently this just means that you can supply bespoke kernel functions for particular applications, but will in future releases also support a range of training algorithms, model selection criteria etc.