Model selection for the LS-SVM. Application to handwriting recognition

The support vector machine (SVM) is a powerful classifier which has been used successfully in many pattern recognition problems. It has also been shown to perform well in the handwriting recognition field. The least squares SVM (LS-SVM), like the SVM, is based on the margin-maximization principle performing structural risk minimization. However, it is easier to train than the SVM, as it requires only the solution to a convex linear problem, and not a quadratic problem as in the SVM. In this paper, we propose to conduct model selection for the LS-SVM using an empirical error criterion. Experiments on handwritten character recognition show the usefulness of this classifier and demonstrate that model selection improves the generalization performance of the LS-SVM.