Abstract: 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 unclassifiable regions that exist in the conventional SVMs.The experimental result indicates that maintaining comparable accuracy，this method is faster than other methods in classification.

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。