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  1. University of Computer Studies, Yangon
  2. Conferences

Trading Department Oriented Web Classification Using Naïve Bayes

http://hdl.handle.net/20.500.12678/0000003813
http://hdl.handle.net/20.500.12678/0000003813
58a7c463-f614-40bb-a87f-5db772c09a5c
e60199aa-c77f-4b13-9f7f-a29b058af90c
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54131.pdf 54131.pdf (279 Kb)
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Article
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Publication
Title
Title Trading Department Oriented Web Classification Using Naïve Bayes
Language en
Publication date 2009-12-30
Authors
Htun, Nilar
Htun, Moe Sanda
Description
Web page classification is significantly different from traditional text classification because of the presence of some additional information, provided by the HTML structure and by the presence of hyperlinks. Web classification is based on a text classification method known as Naïve Bayes. Naïve Bayes is often used in text classification applications and experiments because of its simplicity and effectiveness. In text and web page classification, Bayesian prior probabilities are usually based on term of word frequencies and term counts within a page and its linked pages. This paper presents Naïve Bayes method to classify Web pages by using keywords and defines the respective sections or departments for trading company. This paper is focused on web page representation by text content.
Keywords
Naïve Bayes, Web page classification, text classification
Identifier http://onlineresource.ucsy.edu.mm/handle/123456789/1541
Journal articles
Fourth Local Conference on Parallel and Soft Computing
Conference papers
Books/reports/chapters
Thesis/dissertations
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