Title :
Naive Bayes Web Page Classification with HTML Mark-Up Enrichment
Author :
Fernández, Víctor Fresno ; Herranz, Soto Montalvo ; Unanue, Raquel Martínez ; Rubio, Arantza Casillas
Author_Institution :
ESCET, Univ. Rey Juan Carlos
Abstract :
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
Keywords :
Bayes methods; Internet; classification; hypermedia markup languages; Bayesian prior probability; HTML mark-up information; HyperText Markup Language; Web page representation; Web page term count; Web page term frequency; Web page term relevance; naive Bayes Web page classification; text classification; Bayesian methods; Frequency; HTML; Information resources; Internet; Search engines; Supervised learning; Telecommunication standards; Text categorization; Web pages;
Conference_Titel :
Computing in the Global Information Technology, 2006. ICCGI '06. International Multi-Conference on
Conference_Location :
Bucharest
Print_ISBN :
0-7695-2690-X
Electronic_ISBN :
0-7695-2690-X
DOI :
10.1109/ICCGI.2006.52
