An Optimal Basis for Feature Extraction with Support Vector Machine Classification Using the Radius-Margin Bound

A method is presented for deriving an optimal basis for features classified with a support vector machine. The method is based on minimizing the leave-one-out error which is approximated by the radius-margin bound. A gradient descent method provides a learning rule for the basis in an outer loop of an iteration. The inner loop performs support vector machine training and provides support vector coefficients on which the gradient descent …