A Simplification Algorithm to Support Vector Machines for Regression
回归型支持向量机的简化算法

Aiming at the computational complexity resulted from the large amounts of support vectors when the support vector machines (SVMs) are used in function estimation, a simplification algorithm is presented to reduce the number of support vectors and simplify applications. By the adaptation of the simplification algorithm, the LS-SVM (least square support vector machine) algorithm can be combined with SMO (sequential minimal optimization) algorithm to achieve good results with high learning efficiency and a few number of support vectors.