Constructing an effective text classifier plays a key role in text categorization.
构建一个分类准确的文本分类器是文本分类的关键。
This paper applies BVM to text categorization.
本文将BVM应用于文本分类。
Feature selection is a core research topic in text categorization.
特征选择是文本分类的一个核心研究课题。
KNN algorithm is a common and effective text categorization algorithm.
KNN算法是一种常用的效果较好的文本分类算法。
Addressing this issue, text categorization technology has proposed a series of solutions.
文本分类技术针对这个问题，提出了一系列解决方案。
This paper presents some research on automatic Chinese text categorization system (CATCS).
本文就中文自动文本分类系统进行了一些研究。
Reducing the high dimension of feature vectors is an essential part of text categorization.
对高维的特征集进行降维是文本分类过程中的一个重要环节。
The vectorization of documents affects the speed and accuracy of text categorization greatly.
文档向量化的质量对于文本分类的速度和准确度有着很大的影响。
The more the small probability events happen, the more the impact to text categorization results.
小概率事件发生的情况越多，对文本分类的结果影响也就越大。
This paper presents a new algorithm using title category semantic recognition for text categorization.
本文提出了一种基于标题类别语义识别的文本分类算法。
Automatic text categorization is the assigning of pre - defined category to a new text based on its content.
自动文本分类就是在给定的分类体系下，让计算机根据文本的内容确定与它相关联的类别。
The system can preferably implement automatic text categorization for Chinese pages, and has a higher quality.
该系统可以很好地实现一个中文网页的自动分类，且系统中的分类器具有较高的分类质量。
Text categorization is the foundation of text mining, and feature selection is the core of text categorization.
文本分类是文本挖掘的基础，而特征选择又是文本分类中的核心。
Feature selection is a valid method to reduce the dimension of text vector in automatic text categorization system.
在自动文本分类系统中，特征选择是有效降低文本向量维数的一种方法。
In this article we discussed filter spam methods using text categorization technologies in machine learning fields.
本文使用机器学习理论，讨论了用文本分类的方法来过滤垃圾邮件。
A reduction algorithm based on rough set is improved and then applicated to extract the rules of text categorization.
改进了一种粗糙集决策表的值约简算法，并将其应用到文本分类规则的提取中。
This paper introduces a method on automatic text categorization based on the statistic features of Chinese characters.
提出了一种基于字特征的中文文本分类方法。
We can solve this issue by mapping keywords space to concepts space, and then make text categorization in concept space.
将文本从特征词空间映射到概念空间，进而在概念空间进行文本分类，可以很好地解决这个问题。
The text categorization has become a hot research subject which has attracted a lot of attention but still not solved well.
文本分类成为众所关注但仍未很好解决的热门课题。
This paper proposes a visual words ambiguity analysis method based on text categorization. The codebook is generated by the BOW model.
为解决该问题，提出一种基于文本分类的视觉单词歧义性分析方法。
Moreover, S-TFIDF algorithm is as efficient as TFIDF algorithm, which implies it is competent for large scale text categorization task.
同时，S -TFIDF算法保持了TFIDF算法的高运行效率，适合大规模的文本分类任务。
Text feature extraction and text categorization is the focal point of basic research in the field of intelligent information service system.
文本信息特征提取和文本分类是当前智能信息服务系统基础研究的重点。
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。
Experiment shows that the revised text categorization model meets the need of text categorization, and improves the performance of former one.
实验证明，改进后的文本分类模型适合于文本分类的需要，改善了原有分类器的性能。
Automatic text categorization, which is based on artificial intelligence technology, is the base of how to effectively utilize e-form text data.
基于人工智能技术的自动文本分类是有效利用电子化文本数据的基础。
With the rapid growth of online digital text data, text categorization has become one of the key techniques for handling and organizing text data.
随着在线文本文档数量的快速增长，文本分类已经成为处理和组织文本数据的一种关键技术。
The performance of text categorization algorithm based on centroid is poor when the documents are dispersive or existing more than one peak value.
当文本集较分散或出现多峰值时，基于质心的文本分类算法分类效果很差。
The performance of text categorization algorithm based on centroid is poor when the documents are dispersive or existing more than one peak value.
当文本集较分散或出现多峰值时，基于质心的文本分类算法分类效果很差。
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