文本分类

Web文本分类研究是Web文本挖掘中的一个重要研究内容。

Web text classification is one of the most important research topics .

Web文本分类及其阻塞减少策略

Web text classification and blocking reduction strategies

基于加权类轴的Web文本分类方法研究

Automatic Web Document Classification Based on Category Axis of Terms with Increased Weight

基于Agent的文本分类系统

An Automatic Text Categorization System Based on Agent

一种基于k最近邻的快速文本分类方法

A Fast Text Categorization Approach Based on k - Nearest Neighbor

它主要包括四个方面的内容：Web文本分类、Web文本聚类、信息抽取和信息检索。

This technique contains four aspects : web text classification , web text clustering , information extraction , information retrieval .

基于RBF神经网络和关联规则的Web文本分类规则获取方法

The Web Text Categorization Rule Extraction Based on RBF Neural Network and Association Rule

详细阐述了web文本分类技术、web文本聚类技术和关联规则挖掘技术。

The key techniques used in wed text mining including association rules analyzing , clustering , classification were expounded in detail .

随着文本分类研究及应用的逐步深入，Web分类成为数据挖掘一个重要的研究方向。

With the increasing depth of research and application , Web classification has become an important research direction on data mining .

使用Logistic回归模型进行中文文本分类

Using Logistic regression model for Chinese text categorization

基于boosting的文本分类在股市领域信息抽取系统中的应用

A Boosting-based Text Categorization System and its Application in Information Extraction

基于主动学习SVM的蒙文文本分类系统的设计与实现

The Design and Implement of a Mongolian Text Classifier Based on Active Learning SVM

Web文本分类作为Web文本挖掘中的重要技术，可以在较大程度上解决信息杂乱和信息爆炸的问题。

As the key technology of Web text mining , Web text classification can solve the problem of information disorder and " explosion " to a great extent .

基于Lee模型的文本分类

Text Categorization with Lee Model

一种增量式Bayes文本分类算法

Incremental Bayes Text Categorization Algorithm

随着Internet的快速发展，文本分类已经成为了组织在线信息的核心任务之一，并且成为了许多应用中的关键架构。

With the rapid growth of Internet , text classification has been one of the key tasks of organizing on-line information , and have become the key component of lots of applications .

一个基于朴素贝叶斯方法的web文本分类系统：WebCAT

A Web Document Classifier Based on Naiver Bayes Method : WebCAT

随着Internet的飞速发展，Web文本分类研究已经得到了人们密切的关注，并取得了大量的研究成果。

With the development of Internet at full speed , the research of Web text classification has already got people 's close concern . A large amount of research results have been got .

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。

This paper presents a new method of WEB text categorization rule extraction based on the CHI value theory , rough set theory and decision tree .

一种基于特征聚合理论和LSI的文本分类新方法

A New Method of Text Categorization Based on Feature Aggregation and LSI

提出一种基于知识融合的文本分类算法：语义SVM。

A new text categorization algorithm based on knowledge fusion is suggested which is named Semantic Support Vector Machines ( Semantic SVMs ) .

Web文本分类可以提高用户进行网上信息搜索的效率，可以对搜索结果进行分门别类，帮助用户快速的对目标知识进行定位，并且能够从中抽取有价值的知识。

It can classify search results , which not only enhances the efficiency of search for Web users , but also improves the ability of localization to goal knowledge , and extracts the valuable knowledge .

KNN（K-NearestNeighbour）是向量空间模型中最好的文本分类算法之一。

K - Nearest Neighbour is one of the best text categorization algorithm s.

IRT和规则空间在文本分类中的应用研究

The Research on IRT and Rule Space for Text Classification

最后重点介绍了基于Lee模型的NaiveBayes文本分类方法和基于规则的超文本分类方法。

Finally it focuses on the introduction of Naive Bayes text classification based on Lee model and hypertext classification based on rules .

局部线性与One-Class结合的科技文本分类方法

Journal Text Categorization with the Combination of Local Linearity and One-Class

有了文本分类的工具，用户可以更加方便地阅览Web内容，而且通过限制搜索范围，可以在互联网上尽快查找自己感兴趣的内容。

With the tools of text classification , users can read Web content more easily . Also , by limiting the searching range , one can find the interesting content on the Internet as soon as possible .

Web文本分类可以有效的解决上述问题，它起源于ATC技术（自动文本分类技术），是Web文本挖掘的关键组成部分；

The questions mentioned above can be resolved effectively by Web text classification , which origins from ATC ( Automatic Text Classification ), and is the key constituent of Web text mining .

NaiveBayesian分类器是一种有效的文本分类方法，但由于具有较强的稳定性，很难通过Boosting机制提高其性能。

Naive Bayesian classifier is a kind of effective text categorization methods , but it is hard to improve its performance by Boosting procedure because of its stability .

最后通过实验测试了使用KNN算法的中文Web文本分类技术的效果。

The emphasis of this paper is a method based on the contents & KNN . A furthermore experiment tests the effects of the categorization techniques of Chinese Web texts .