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.
出处
《计算机工程与应用》
CSCD
北大核心
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)~~
关键词
支持向量机
二叉树
人脸识别
Support Vector Machine
binary tree
face recognition
