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 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.

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In: Proceedings of the Ninth International Conference on Artificial Neural Networks

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