Abstract

In this paper we introduce two formulations for training support vector machines using linear programming. The formulations are based on considering the L1 and L1 norms instead of the currently used L2 norm, and maximising the margin between the separating hyperplane and the two data sets using L1 and L1 distances. The fact that the separation problem becomes solvable by a standard linear programming software opens a wide range of applications, for example cases where the training speed is crucial, or the data sets are too large for quadratic programming software to be used. We report results obtained for some standard benchmark problems, and compare them with those obtained by other methods, conrming that the performance of all the formulations is similar. 1 1 Introduction Linear programming approaches to support vector machines have recently been addressed [1, 3, 10]. Their main advantage concerns the possibility of using solvers for linear problems, with improved reliability and...

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