Getting to grips with support vector machines: Application
The support vector machine (SVM) is a technique for function estimation which was proposed in the early 1990s. This paper is a follow-up to the paper Getting to grips with Support Vector Machines: Theory, which gave an introductory overview of SVMs. This paper discusses issues arising when applying the SVM in practice, giving useful hints and suggestions for newcomers to the field. The focus is on the SVM classifier, although the support vector technique is also applicable to regression and other techniques.