摘要
两类支撑向量机(SVM)用于模式识别具有最优的推广能力.对于常见的多类识别问题,需要构造多类SVM.本文提出一种新的基于决策树的构造方法,由此构成的多类SVM (DTSVM),与现有的方法相比,具有更快的计算速度,适用于需处理样本数较多的识别问题.
Support Vector Machines (SVM) can achieve good performance when applied to small-sample pattern recognition problems. The basic support vector machine is for two-class problem. In this paper, the principle of SVM is introduced and a new support vector machine, called decision tree based SVM (DTSVM), is proposed to solve multi-class recognition problems. In the decision tree, each non-leaf node represents a SVM classifier. The decision tree is constructed by hierarchical clustering or by a priori knowledge. DTSVM is proved to be the fastest method in our experimental evaluation. It is applicable to multi-class recognition problems with a large amount of samples.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2002年第2期178-181,共4页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金(No.69971004)
