一种新的基于二叉树的SVM多类分类方法  

A new SVM multiclass classification based on binary tree

作　　者：孟媛媛[1] 刘希玉[1] 

机构地区：[1]山东师范大学信息管理学院,山东济南250014

出　　处：《计算机应用》2005年第11期2653-2654,2657,共3页journal of Computer Applications

基　　金：山东省自然科学基金资助项目(Z2004G02);山东省中青年科学家奖励基金项目(03BS003)

摘　　要：介绍了几种常用的支持向量机多类分类方法,分析其存在的问题及缺点。提出了一种基于二叉树的支持向量机多类分类方法(BT-SVM),并将基于核的自组织映射引入进行聚类。结果表明,采用该方法进行多类分类比1-v-r SVMs和1-v-1 SVMs具有更高的分类精度。The problems and defections of the existing methods of SVM multi-class classification were analyzed. A multiclass classification based on binary tree was put forward. A modified self-organization map （ SOM）, KSOM （ kernel-based SOM）, was introduced to convert the multi-class problem into binary tress, in which the binary decisions were made by SVMs. The results show that it has higher muhiclass classification accuracy than the multi-class SVM approaches with ＂one-versusone＂ and ＂one-versus-the rest＂.

关 键 词：多类分类 支持向量机 二叉树 自组织映射 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TP391[自动化与计算机技术—控制科学与工程]