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有向无环图支持向量(DAG-SVMS)多类分类方法,是一种新的多类分类方法。该方法采用了最小超球体类包含作为层次分类依据。试验结果表明,采用该方法进行多类分类,跟已有的分类方法相比有更高的分类精度。

This is SvmFu, a package for training and testing support vector machines (SVMs). It s written in C++. It uses templates. The advantage of templates is that the types of kernel values and data points can be varied to suit the problem.

多分类支持向量机实现方法的分析比较：一对一、一对多和DAG的对比，比较专业，

用C语言实现了编译系统中的DAG算法。 处理的比较简单。

Dag Erling http library source code

New training algorithm for linear classification SVMs that can be much faster than SVMlight for large datasets. It also lets you direcly optimize multivariate performance measures like F1-Score, ROC-Area, and the Precision/Recall Break-Even Point.

MSJ-06Ⅱ-A 美国UNION UNION FLAG 型式番号：SVMS-01 欧洲联盟AEU AEU ENACT 型式番号：AEU-09 AEU ENACT（量产型） 型式编号：AEU-09

This example demonstrates how to use WEKA s SVMs classifier in Matlab.

这个课程项目完成了给定DAG graph，找到所有拓扑排序并且输出。用到了指针和链表。对于学习C/C++和数据结构比较有帮助。

In this paper we present a classifier called bi-density twin support vector machines (BDTWSVMs) for data classification. In the training stage, BDTWSVMs first compute the relative density degrees for all training points using the intra-class graph whose weights are determined by a local sc ...