中文摘要：

决策导向无环图支持向量机（DDAGSVM）是一种典型的SVM多类分类算法,然而传统SVM决策分类器存在误差积累,其推广能力有待进一步提高。为改进DDAGSVM,有效的做法是定义一种类间可分离性测度,将容易分的类先分割出来,然后再分不容易分的类,使错分尽可能地远离图的根部。引入了一种基于广义KKT条件的类间可分离性测度,提出一种改进的DDAGSVM分类决策算法。三螺旋线实验和HRRP分类实验证明该方法对控制分类错误有明显的效果。

英文摘要：

A decision directed acyclic graph support vector machine is a typical multi-class classification with support vector machines. But error accumulation exists in the traditional decision classification, and its generalization ability depends on the tree structure. In this paper, to improve the generalization ability of DDAGSVM, a novel separable measure is defined based on the generalized KKT, and an improved decision directed acyclic graph support vector machine is given. The three-spiral and HRRP experimental results show that this kind of algorithm has an obvious effectiveness in controlling classification errors.