Support Vector Machines

Abstract

Classifying data is a common task in data-driven modeling. Using support vector machines, we can separate classes of data by a hyperplane. A support vector machine (SVM) is a concept for a set of related supervised learning methods that analyze data and recognize patterns, used for classification and regression analysis. The formulation of SVM uses the structural risk minimization principle, which has been shown to be superior to the traditional empirical risk minimization principle used by conventional neural networks. This chapter presents principles of classification and regression analysis by support vector machines, briefly. Also related MATLAB programs are presented.