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J. Weston and R. Herbrich (2000)

# Adaptive Margin Support Vector Machines

In: Advances in Large Margin Classifiers, ed. by A.J. Smola and P.L. Bartlett and B. Schölkopf and D. Schuurmans, pp. 281-296, Cambridge, MA, MIT Press.

Based on a leave-one-out bound of Jaakola and Haussler a modification of the original SV algorithm is devised in order to minimize the bound directly. This formulation is essentially parameter free, maintains sparsity of the solution, and can be solved by a linear program. The novelty can be found in the fact that rather than maximizing the overall minimum margin, the individual margin of patterns is maximized adaptively. Experiments show that its classification performance is very competitive with an optimally adjusted SV machine and comparable to a $\nu$-SV classifier. Uniform convergence bounds are provided.