Fast training of Support Vector Machines for regression

We propose a fast way to perform the gradient computation in Support Vector Machine (SVM) learning, when samples are positioned on an m-dimensional grid. Our method takes advantage of the particular structure of the constrained quadratic programming problem arising in this case. We show how such structure is connected to the properties of block Toeplitz matrices and how they can be used to speed-up the computation of matrix-vector products.