可保证分类性能的最小二乘支持向量机

摘要

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.%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.