摘要
基于SVM(Support Vectort Machine)理论的分类器已经发展为一种通用的二值分类器,但它不适用于多值的场合。在分析经典的SVM分类算法和决策树分类算法的基础上,提出了将SVM和二叉决策树结合的方法来实现多值分类器(SVMDT),并将其应用于文本分类,实验表明在分类精度和速度上具有良好的性能。
Classifier based on SVM(Support Vectort Machine) theory has been developed as a general-purpose two-class classifier, but it is not suitable for multi-class classification. In the paper two basic classification algorithms are analyzed and combined them into one multi-class classifier(SVMDT).It is applied in text classification and the experimental results show that it has good performance in classification precision and test speed.
出处
《计算机应用研究》
CSCD
北大核心
2004年第1期23-24,43,共3页
Application Research of Computers
基金
国家自然科学基金资助项目(60273087)
关键词
支持矢量机
二叉决策树
多值分类器
文本分类
SVM( Support Vectort Machine)
Binary Decision Tree
Multi-class Classifier
Text Classification