Improving the Accuracy and Speed of Support Vector Machines

Support Vector Learning Machines (SVM) are nding application in pattern recognition, regression estimation, and operator inversion 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 interest. In this paper we combine two such techniques on a pattern recognition problem. The method for improvinggeneralization performance (the \virtual support vector" method) does so by incorporating known invariances of the problem. This method achieves

• Type: Conference
• Year: 1996
• Where: NIPS
• Authors: Christopher J. C. Burges, Bernhard Schölkopf
• Keywords: NIPS 1996, NIPS 2007, Pattern Recognition, Support Vector, Vector Learning Machines