Training invariant support vector machines

Practical experience has shown that in order to obtain the best possible pe rformance, prior knowledge about invariances of a classification problem at hand ought to be incorporated into the training procedure. We describe and review all known methods for doing so in support vector machines, provide experimental results, and discuss their respective merits. One of the signi ficant new results reported in this work is our recent achievement of the l owest reported test error on the well-known MNIST digit recognition benchma rk task, with SVM training times that are also significantly faster than pr evious SVM methods.