朴素贝叶斯网页分类与HTML标记充实

2006 International Multi-Conference on Computing in the Global Information Technology - (ICCGI'06) Pub Date : 2006-08-01 DOI:10.1109/ICCGI.2006.23

V. F. Fernández, S. M. Herranz, R. M. Unanue, A. C. Rubio

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引用次数: 16

摘要

在文本和Web页面分类中，贝叶斯先验概率通常基于词频率、一个页面内和所有页面中的词计数。但是，Web页面表示中的新方法使用HTML标记信息来查找Web页面中的术语相关性。本文针对这些方法提出了一个朴素贝叶斯网页分类系统

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Naive Bayes Web Page Classification with HTML Mark-Up Enrichment

In text and Web page classification, Bayesian prior probabilities are usually based on term frequencies, term counts within a page and among all the pages. However, new approaches in Web page representation use HTML mark-up information to find the term relevance in a Web page. This paper presents a naive Bayes Web page classification system for these approaches

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2006 International Multi-Conference on Computing in the Global Information Technology - (ICCGI'06)

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