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Improving the accuracy and speed of support vector learning machines


Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Burges, C., & Schölkopf, B. (1997). Improving the accuracy and speed of support vector learning machines. Advances in Neural Information Processing Systems, 375-381.

Support Vector Learning Machines (SVM) are finding application in pattern recognition, regression estimation, and operator inversion for illposed problems. Against this very general backdrop any methods for improving the generalization performance, or for improving the speed in test phase of SVMs are of increasing interest. In this paper we combine two such techniques on a pattern recognition problem The method for improving generalization performance the "virtual support vector" method does so by incorporating known invariances of the problem This method achieves a drop in the error rate on 10.000 NIST test digit images of 1,4 to 1 . The method for improving the speed (the "reduced set" method) does so by approximating the support vector decision surface. We apply this method to achieve a factor of fifty speedup in test phase over the virtual support vector machine The combined approach yields a machine which is both 22 times faster than the original machine, and which has better generalization performance achieving 1,1 error. The virtual support vector method is applicable to any SVM problem with known invariances The reduced set method is applicable to any support vector machine.