Adaptive margin support vector machines for classification
Ralf Herbrich · 1999
In this paper we propose a new learning algorithm for classification learning based on the Support Vector Machine (SVM) approach. Existing approaches for constructing SVMs [12] are based on minimization of a regularized margin loss where the margin is treated equivalently for each training pattern. We propose a reformulation of the minimization problem such that adaptive margins for each training pattern are utilized, which we call the Adaptive Margin (AM--) SVM. We give bounds on the generalization error of AM--SVMs which justify their robustness against outliers, and show experimentally that the generalization error of AM--SVMs is comparable to classical SVMs on benchmark datasets from the UCI repository. 1 Introduction Recently, the study of classification learning has shown that algorithms which learn a real--valued function for classification can control their generalization error by making use of a quantity known as the margin. Based on these results, Support Vector Machines w...