Study of Least Squares Support Vector Machines
最小二乘支持向量机算法研究

In this paper, we present a least squares version for support vector machines(SVM)classifiers and function estimation. Due to equality type constraints in the formulation, the solution follows from solving a set of linear equations, instead of quadratic programming for classical SVM. The approach is illustrated on a two-spiral benchmark classification problem. The results show that the LS-SVM is an efficient method for solving pattern recognition.