On Properties of Support Vector Machines for Pattern Recognition in Finite Samples

Abstract

The support vector machine proposed by Vapnik belongs to a class of modern statistical learning methods based on convex risk minimization. Other special cases are AdaBoost, kernel logistic regression and least squares. The support vector machine has the advantage that it usually leads to a reduction of complexity, because only the support vectors and not a observations contribute to the prediction of a new response. This paper addresses robustness properties of the support vector machine for pattern recognition in finite samples. Sensitivity curves in the sense of J.W. Tukey are used to investigate the possible impact of a single data point.