A Text Classification Method Based on Hybrid Probability Model

WU Xin-ling

Chinese

您当前的位置：

首页 >

文章列表页 >

A Text Classification Method Based on Hybrid Probability Model

更新时间：2026-09-04

- A Text Classification Method Based on Hybrid Probability Model
- Vol. 28, Issue 11, Pages: 133-136(2011)
- 作者机构：
  
  广东技术师范学院机构计算机科学学院,广东,广州,510262
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2011
- 稿件说明：
移动端阅览
吴新玲. 一种基于混合概率模型的文本分类方法[J]. 微电子学与计算机, 2011, 28(11): 133-136.

WU Xin-ling. A Text Classification Method Based on Hybrid Probability Model[J]. 2011, 28(11): 133-136.
吴新玲. 一种基于混合概率模型的文本分类方法[J]. 微电子学与计算机, 2011, 28(11): 133-136. DOI：

WU Xin-ling. A Text Classification Method Based on Hybrid Probability Model[J]. 2011, 28(11): 133-136. DOI：

摘要

提出了一种基于类的混合概率分类方法.该方法为每一类文本独立选取能代表其本质特性的主要特征

即不同类型的文本由不同的主要特征表示

并基于各类的主要特征分别为每类文本建立相应的概率分布模型

然后再根据朴素贝叶斯方法对未知类型的文本进行分类.实验结果表明:该方法简单有效且易于实现.

Abstract

A hybrid probabilistic classification method was proposed based on the class.The method selects the main features that can represent the essential characteristics of a class text independently for every class text.That is different class text will be expressed by different main features.Based on the main features of each class text

the probability distribution model will be established respectively for every class text.Then the unknown class label text can be classified by naive Bayesian method.The experiment results show that the proposed method is simple

effective and easy to implement.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

An Improved KNN Text Categorization Method Based on Data Uneven

A Mixed Text Reducing Feature Method Based on Sort

Monte carlo analysis of V-layout trench SiC neutron detector

Steganography model guided by attention mechanism and improved dense convolution

A feature-oriented context understanding and slice analysis network for tuberculosis detection

Related Author

LIU Hai-feng

CHEN Qi

LIU Shou-sheng

SU Zhan

LIU Hai-feng

LIU Shou-sheng

YAO Ze-qing

Related Institution

Institute of Sciences, PLA University of Science and Technology

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰