Support vector machines in analysis of top quark production

The Support Vector Machine (SVM) learning algorithm is a new alternative to multivariate methods such as neural networks. Potential applications of SVMs in high energy physics include the common classification problem of signal/background discrimination as well as particle identification. A comparison of a conventional method and an SVM algorithm is presented here for the case of identifying top quark events in Run II physics at the CDF experiment.