Multivariate data analysis techniques have the potential to improve physics
analyses in many ways. The common classification problem of signal/background
discrimination is one example. The Support Vector Machine learning algorithm is
a relatively new way to solve pattern recognition problems and has several
advantages over methods such as neural networks. The SVM approach is described
and compared to a conventional analysis for the case of identifying top quark
signal events in the dilepton decay channel amidst a large number of background
events.Comment: 8 pages, 8 figures, to be published in the proceedings of the
"Advanced Statistical Techniques in Particle Physics" conference in Durham,
UK (March, 2002