Abstract: Least squares support vector machines(LS-SVM) is a new support vector machine. 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. This paper presents a multiclass least squares machines for classification while LS-SVM is only for the case of two classes in the past. Furthermore it inducts new regularization and capacity control for it which improves accuracy and convergence of classification. The approach is illustrated on a four-spiral benchmark classification problem. The results show that the multiclass LS-SVM is an efficient classifier for solving pattern recognition.

摘要： 最小二乘支持向量机采用最小二乘线性系统代替传统的支持向量机采用二次规划方法解决模式识别问题。该文详细推理和分析了二元分类最小二乘支持向量机算法，构建了多元分类最小二乘支持向量机，并通过典型样本进行测试，结果表明采用多元分类最小二乘支持向量机进行模式识别是有效、可行的。

关键词: 机器学习, 支持向量机, 模式识别, 最小二乘支持向量机, 神经网络

CLC Number: 

• TP181