计算机应用研究2012，Vol.29Issue(1)：155-157,3.DOI:10.3969/j.issn.1001-3695.2012.01.044
结合聚类思想神经网络文本分类技术研究
Text classification algorithm research based on clustering and neural network
摘要
Abstract
This paper analyzed the general model of text categorization system technology, after the application of the mutual information feature extraction, proposed a text classification algorithm based on a sample center radiate basis function (RBF) neural network, and introduced the core idea of clustering algorithm, to improve the back-propagation (BP) neural network classification algorithm convergence slower shortcomings. Experimental results show that, compared with BP network,RBF network not only has higher speed and stronger nonlinear mapping capacity t but also has faster convergence speed and better accuracy on classification effect. Combining the clustering thought, RBF text classification algorithm has larger theory research value and practical application prospect.关键词
文本分类/神经网络/聚类算法/互信息量Key words
text classification/ neural network/ clustering algorithm/ mutul information分类
信息技术与安全科学引用本文复制引用
朱云霞..结合聚类思想神经网络文本分类技术研究[J].计算机应用研究,2012,29(1):155-157,3.基金项目
江苏省高校自然科学研究计划资助目 （）
