Adaptive margin support vector machines for classification
We propose a learning algorithm for classification learning based on the support vector machine (SVM) approach. Existing approaches for constructing SVMs 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.