document: Proximal support vector machine classifiers Instead of a standard support vector machine ( SVM ) that classifies points by assigning them to one of two disjoint half-spaces , points are classified by assigning them to the closest of two parallel planes ( in input or feature space ) that are pushed apart as far as possible . This formulation , which can also be interpreted as regularized least squares and considered in the much more general context of regularized networks ( 8 , 9 ) , leads to an extremely fast and simple algorithm for generating a linear or nonlinear classifier that merely requires the solution of a single system of linear equations . In contrast , standard SVMs solve a quadratic or a linear program that require considerably longer computational time . Computational results on publicly available datasets indicate that the proposed proximal SVM classifier has comparable test set correctness to that of standard SVM classifiers , but with considerably faster computational time that can be an order of magnitude faster . The linear proximal SVM can easily handle large datasets as indicated by the classification of a 2 million point 10-attribute set in 20.8 seconds . All computational results are based on 6 lines of MATLAB code .
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