Abstract: Support Vector Machine（SVM） is one of focuses of research and application in classification.A new least-squares-based algorithm that introduces a within-class scatter with guaranteed classification performance（VSLSVM） in the design of least squares support vector machines（LS-SVM） is presented.This algorithm can obtain better correctness that reformulates primal LS-SVM problems with optimality criterion Min w′Mw where w is the weight vector corresponding the primal LS-SVM problems，M is the within-class scatter matrix.This method only requires to solve a linear system instead of a quadratic programming problem.Experiments are included to compare SVM and Suykens’ approach.

Key words: east squares support vector machines（LS-SVM）, with-class scatter, better correctness, linear system

摘要： 当前支持向量机是分类研究与应用的一个热点。提出了一个新的最小二乘支持向量机算法，该算法向最小二乘支持向量机（LS-SVM）优化模型中融入了类内散度（VSLSVM）思想，即用优化准则Min w′Mw对原LS-SVM进行重组合，w为对应LS-SVM中的权向量，M是类内散度矩阵。提出的方法仅仅需要求解一个线性系统而不是凸规划问题，实验主要对SVM和Suykens等人的方法进行了比较，并验证了提出的算法的有效性。

关键词: 最小二乘支持向量机, 类内散度, 更好精度, 线性系统