摘要
在这份报纸，我们与传统的 DAGSVM 为多班 classification.Compared 建议一台改进的指导的非循环的图支持向量机器( DAGSVM )，改进版本有指导的非循环的图的结构没被选择的优点随机、修理，并且根据到来的测试样品最佳能是适应的，因此，它有好归纳 performance.From 六数据集的实验，我们能看到 DAGSVM 的建议改进版本比 tr
In this paper, we propose an improved Directed Acyclic Graph Support Vector Machine （DAGSVM） for multi-class classification. Compared with the traditional DAGSVM, the improved version has advantages that the structure of the directed acyclic graph is not chosen random and fixed, and it can be adaptive to be optimal according to the incoming testing samples, thus it has a good generalization performance. From experiments on six datasets, we can see that the proposed improved version of DAGSVM is better than the traditional one with respect to the accuracy rate.
