We propose here a fast way to perform the gradient computation in Support Vector Machine (SVM) learning, when samples are positioned on a 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
Fast training of Support Vector Machines for Regression
ANGUITA, DAVIDE;
2000-01-01
Abstract
We propose here a fast way to perform the gradient computation in Support Vector Machine (SVM) learning, when samples are positioned on a 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
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