Improving the Accuracy and Speed of Support Vector Machines

Support Vector Learning Machines (SVM) are finding application in pattern recognition, regression estimation, and operator inver(cid:173) sion for ill-posed 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 in(cid:173) terest. In this paper we combine two such techniques on a pattern recognition problem. The method for improving generalization per(cid:173) formance (the "virtual support vector" method) does so by incor(cid:173) porating 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 1.0%.