  We review the concept of support vector machines (SVMs) and discuss examples
of their use. One of the benefits of SVM algorithms, compared with neural
networks and decision trees is that they can be less susceptible to over
fitting than those other algorithms are to over training. This issue is related
to the generalisation of a multivariate algorithm (MVA); a problem that has
often been overlooked in particle physics. We discuss cross validation and how
this can be used to improve the generalisation of a MVA in the context of High
Energy Physics analyses. The examples presented use the Toolkit for
Multivariate Analysis (TMVA) based on ROOT and describe our improvements to the
SVM functionality and new tools introduced for cross validation within this
framework.
