Sample reduction based on kernel squared Mahalanobis distance for support vector machines

This paper presents a sample reduction algorithm based on kernel squared Mahalanobis distance, as a sampling preprocessing for SVM training to improve the scalability. Experimental results show that, the proposed algorithm is effective for reducing training samples for nonlinear SVMs.