| 支持向量机和蚁群算法的网页分类研究 |
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| 引用本文： | 宋军涛,周铜,杜庆灵.支持向量机和蚁群算法的网页分类研究[J].计算机工程与应用,2009,45(17):122-124. |
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| 作者姓名： | 宋军涛 周铜 杜庆灵 |
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| 作者单位： | 1. 河南工业大学,信息科学与工程学院,郑州,450001
2. 中州大学,郑州,450052 |
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| 摘 要： | 网页分类技术是Web数据挖掘的基础与核心,是基于自然语言处理技术和机器学习算法的一个典型的具体应用。基于统计学习理论和蚁群算法理论,提出了一种基于支持向量机和蚁群算法相结合的构造网页分类器的高效分类方法,实验结果证明了该方法的有效性和鲁棒性,弥补了仅利用支持向量机对于大样本训练集收敛慢的不足,具有较好的准确率和召回率。
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| 关 键 词： | 网页分类 蚁群算法 支持向量机 召回率 准确率 |
| 收稿时间： | 2008-12-29 |
| 修稿时间： | 2009-3-2
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Study of categorization of Web-page of support vector machine and ant colony algorithm |
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| SONG Jun-tao,ZHOU Tong,DU Qing-ling.Study of categorization of Web-page of support vector machine and ant colony algorithm[J].Computer Engineering and Applications,2009,45(17):122-124. |
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| Authors: | SONG Jun-tao ZHOU Tong DU Qing-ling |
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| Affiliation: | 1.Department of Information Science and Engineering，Henan University of Technology，Zhengzhou 450001，China 2.ZhongZhou University，Zhengzhou 450052，China |
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| Abstract: | Web page categorization is the foundation and core problem of web data mining，it is a typical application based on technology of natural language processing and machine learning.It is imperative to find an effective and efficient method for web page categorization.In this paper，a new method is proposed for web page categorization based on ant colony optimization algorithm（ACOA） and support vector machines（SVMs）.The experimental results show that the method is effective and robust，only to make up for the use of support vector machines for large sample training set less than the slow convergence with better precision and recall. |
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| Keywords: | web page categorization ant colony algorithm（ACA） support vector machine（SVM） recall precision |
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