Training a Support Vector Machine in the Primal
Neural Computation
Building Support Vector Machines with Reduced Classifier Complexity
The Journal of Machine Learning Research
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In this article we present a new class of separating hyperplane methods for the binary classification task. Our hyperplanes have a very low Vapnik-Chervonenkis dimension, so they generalise well. Geometrically, our approach is based on searching of a proper pair of observations from different classes of the explained variable. Once this pair is found the discriminant hyperplane becomes orthogonal to the line connecting these observations. This method allows the direct optimisation of any prediction criterion, not necessary the fraction of correctly classified observations. Models generated by this technique have low computational complexity and allow fast classification. We illustrate the performance of our method by applying it to the problem of optimisation of direct marketing campaigns, where the natural measure of the prediction performance is the lift curve.