This paper presents a new method of WEB text categorization rule extraction based on the CHI value theory, rough set theory and decision tree.
本文根据CHI值原理、粗集理论和决策树原理，提出了一种抽取Web文本分类规则的新方法。
Finally, based on basic research of multi-class categorization of SVM, the text applies the theory to practice and analyzes a web text categorization model based on SVM network.
最后在完成基于SVM多类别分类的理论研究的基础上，将其理论应用于实践，构建了一个了基于SVM的网络文本分类模型。
The traditional KNN has a fatal defect that time of similarity computing is huge. The practicality will be lost when the KNN is applied to WEB text categorization with high dimension and huge samples.
传统KNN方法的明显缺陷是样本相似度的计算量很大，使其在具有大量高维样本的Web文本分类中缺乏实用性。
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