Least squares support vector machine classifiers: an empirical evaluation

In this paper, we evaluate least squares support vector machine (LS-SVM) classifiers with RBF kernels on five publicly available reallife benchmark DCI data sets. While standard SVM optimisation involves solving quadratic or linear programming problems, the least squares version corresponds to solving a set of linear equations, due to equality constraints in the problem formulation of the SYM. Very promising results are reported indicating the good generalization behavior of the estimated RBF LS-SYM classifiers. For many large scale real life applications least squares support vector machines in combination with the tuning technique presented in this paper may offer a fast and simple method for obtaining classifiers with good generalization performance.

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