SVM Tutorial - Classification, Regression and Ranking

Support Vector Machines(SVMs) have been extensively researched in the data mining and machine learning communities for the last decade and actively applied to applications in various domains. SVMs are typically used for learning classification, regression, or ranking functions, for which they are called classifying SVM, support vector regression (SVR), or ranking SVM (or RankSVM) respectively. Two special properties of SVMs are that SVMs achieve (1) high generalization by maximizing the margin and (2) support an efficient learning of nonlinear functions by kernel trick. This chapter introduces these general concepts and techniques of SVMs for learning classification, regression, and ranking functions. In particular, we first present the SVMs for binary classification in Section 2, SVR in Section 3, ranking SVM in Section 4, and another recently developed method for learning ranking SVM called Ranking Vector Machine (RVM) in Section 5.