Extract candidates of support vector from training set

This paper proposes a heuristic method to extract candidates of support vector from training set. Training a support vector on the extracted candidates, we attain good generalization on test set. It shows that candidates of support vector contain almost all the necessary information to solve a given classification task. This method is also applied to incorporate prior knowledge into support vector machines. Experiments on digits recognition show the same performance as virtual support vector method.