非平衡二叉树多类支持向量机分类方法  

Multi-class SVM method based on non-balanced binary tree

作　　者：夏思宇[1] 潘泓[1] 金立左[1] 

机构地区：[1]东南大学自动化学院,南京210096

出　　处：《计算机工程与应用》2009年第17期167-169,共3页Computer Engineering and Applications

基　　金：国家自然科学基金(No.60805002;No.90820009);航空科学基金项目(No.20081069003)~~

摘　　要：提出一种新的基于非平衡二叉树的支持向量机多类别分类方法。该方法通过分析已知类别样本的先验分布知识,构造一个二叉决策树,使容易区分的类别从根节点开始逐层分割出来,以获得较高的推广能力。该方法解决了传统分类算法中所存在的不可分区域问题,在训练时只需构造N-1个SVM分类器,而测试时的判决次数小于N。将该方法应用于人脸识别实验。测试结果表明,与传统分类算法相比,该方法的平均分类时间是最少的。In this paper,a non-balanced binary tree is proposed for extending support vector machines（SVM） to multi-class problems.The non-balanced binary tree is constructed based on the prior distribution of samples ,which can make the more separable classes separated at the upper node of the binary tree.For an N class problem,this method only needs N-1 SVM classifiers in the training phase,while it has less than N binary test when making a decision.Further,this method can avoid the unelassifiable regions that exist in the conventional SVMs.The experimental result indicates that maintaining comparable accuracy,this method is faster than other methods in classification.

关 键 词：支持向量机 二叉树 人脸识别 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]