Abstract: To resolve the unclassifiable regions and to elevate generalization ability of multi-category support vector machines at present，an improved multi-category SVM based on Binary Tree（BT-SVM） is proposed.The improved algorithm defines the decision-radius which comes from the category-hypersphere-radius that is divided into kernel-radius and max-radius based on Pareto principle.The final BT’s structure is created by the decision-radius that can make the category which has wide-distribution and small-scatter on the top of BT，therefore the measurement errors are avoided.Numerical experiments on standard dataset demonstrate that the improved BT-SVM can better deal with practical multi-category classification problems with more adaptive ability.

摘要： 为解决现有支持向量机多类分类算法的不可分区域问题及提高泛化能力，提出一种改进的基于二叉树结构的支持向量机多类分类算法。该算法基于帕累托原则，将类超球体半径分解成核心半径和最小半径，通过两者加权计算最终的类超球体决策半径，并以此半径大小为依据生成二叉树结构。该算法避免了测量所引入的误差，使得样本分布广散布小的类处于二叉树的上层节点，从而获得更大的划分空间。实验结果表明：该算法具有一定的适应能力，能更好地解决实际多类分类问题。