STAR - Sparsity through Automated Rejection

Heuristic methods for the rejection of noisy training examples in the support vector machine (SVM) are introduced. Rejection of training errors, either ooine or online, results in a sparser model that is less aaected by noisy data. A simple ooine heuristic provides sparser models with similar generalization performance to the standard SVM, at the expense of longer training times. An online approximation of this heuristic reduces training time and provides a sparser model than the SVM with a slight decrease in generalization performance.