Robust regression with extreme support vectors

Abstract

Extreme Support Vector Machine (ESVM) is a nonlinear robust SVM algorithm based on regularized least squares optimization for binary-class classification. In this paper, a novel algorithm for regression tasks, Extreme Support Vector Regression (ESVR), is proposed based on ESVM. Moreover, kernel ESVR is suggested as well. Experiments show that, ESVR has a better generalization than some other traditional single hidden layer feedforward neural networks, such as Extreme Learning Machine (ELM), Support Vector Regression (SVR) and Least Squares-Support Vector Regression (LS-SVR). Furthermore, ESVR has much faster learning speed than SVR and LS-SVR. Stabilities and robustnesses of these algorithms are also studied in the paper, which shows that the ESVR is more robust and stable.