一种新的基于ART的支持向量机多类分类方法

基于支持向量机的二值分类原理，提出了一种由自适应共振理论方法与支持向量机相结合的改进型多类分类方法,此方法改进了传统支持向量机的一对一多类分类方法；对于每个二值分类器的结果进行决策时没有采用投票原则，而是采用自适应共振理论网络融合二值分类器的输出信息，从而克服了当分类器输出结果接近于O时投票法容易出现决策错误和票数相同时无法决策的不足．此算法已应用于玻璃的分类．仿真实验证明，此方法具有较好的分类效果．

A Novel SVM Multi-class Classification Based on ART

Based on the principle of binary classification of support vector machine (SVM), an improved multi-class classification method is developed, which combines the adaptive resonance theory with SVM. The proposed approach improves the one-against-one classifying algorithm of traditional SVMs. In the decision-making process of the results of each binary classifier, the voting principle is not adopted; instead the adaptive resonance theory is used to fuse the output of each binary classifier. Thus this method avoids the existence of fusing errors when the binary classifier outputs approach zero, and overcomes the problem of refusing to fuse when the algorithm gets the same votes. The algorithm has been applied to glass-classification. Simulation experiments prove that the classification results are more accurate.