Adaptive Margin Support Vector Machines
In this chapter we present a new learning algorithm, Leave{One{Out (LOO{) SVMs and its generalization Adaptive Margin (AM{) SVMs, inspired by a recent upper bound on the leave{one{out error proved for kernel classiiers by Jaakkola and Haussler. The new approach minimizes the expression given by the bound in an attempt to minimize the leave{one{out error. This gives a convex optimization problem which constructs a sparse linear classiier in feature space using the kernel technique. As such the algorithm possesses many of the same properties as SVMs and Linear Programming (LP{) SVMs. These former techniques are based on the minimization of a regularized margin loss, where the margin is treated equivalently for each training pattern. We propose a minimization problem such that adaptive margins for each training pattern are utilized. Furthermore, we give bounds on the generalization error of the approach which justiies its robustness against outliers. We show experimentally that the generalization error of AM{SVMs is comparable to SVMs and LP{SVMs on benchmark datasets from the UCI repository.