| 基于支持向量机集成的分类 |
| |
| 引用本文： | 魏玲,张文修.基于支持向量机集成的分类[J].计算机工程,2004,30(13):1-2,17. |
| |
| 作者姓名： | 魏玲 张文修 |
| |
| 作者单位： | 1. 西安交通大学理学院,信息与系统科学研究所,西安,710049;西北大学数学系,西安,710069
2. 西安交通大学理学院,信息与系统科学研究所,西安,710049 |
| |
| 摘 要： | 支持向量机是一种基于结构风险最小化原理的分类技术，本文提出了将支持向量机分类器进行集成的分类思想。首先．在原始样本的基础上形成子支持向量机，得到待检样本的子预测；进而对子预测进行适当的组合，以确定样本最终的类别预报。模拟实验结果表明，该方法具有明显优于单一支持向量机的更高的分类准确率。
|
| 关 键 词： | 支持向量机 结构风险最小化 集成 子支持向量机 子预测 |
| 文章编号： | 1000-3428(2004)13-0001-02 |
Classification Based on SVM Ensemble |
| |
| WEI Ling,ZHANG Wenxiu.Classification Based on SVM Ensemble[J].Computer Engineering,2004,30(13):1-2,17. |
| |
| Authors: | WEI Ling ZHANG Wenxiu |
| |
| Affiliation: | WEI Ling1,2,ZHANG Wenxiu1 |
| |
| Abstract: | The support vector machine (SVM) is a classification technique based on the structural risk minimization principle. A new classificatio method, support vector machine ensemble, is proposed in this paper. This method includes two procedures. Firstly, it gives the sub-forecast of a new sampl using sub-SVM which is obtained by Bagging or Boosting, and then, combine these sub-forecasts to decide the final class. Compared with the single suppo vector machine method, the support vector machine ensemble method has better classification accuracy. |
| |
| Keywords: | Support vector machine Structural risk minimization Ensemble Sub-SVM Sub-forecast |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
|
