Relevance Sanguk Noh, Youngsoo Choi, Haesung Seo, Kyunghee Choi, Gihyun...  @ NUS Home/Search
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Abstract: It is indispensable that the users surfing on the Internet could have web pages classified into a given topic as correct as possible. Toward this ends, this paper presents a topic-specific crawler computing the degree of relevance and refining the preliminary set of related web pages using term frequency/ document frequency, entropy, and compiled rules. In the experiments, we test our topic-specific crawler in terms of the accuracy of its classification, the crawling efficiency, and the... (Update)
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BibTeX entry: (Update)
@misc{ noh-intelligent,
author = "Relevance Sanguk Noh",
title = "An Intelligent Topic-Specific Crawler Using Degree of",
url = "citeseer.comp.nus.edu.sg/707277.html" }
Citations (may not include all citations):2177 Programs for Machine Learning (context) - Quinlan - 1993
1447 A mathematical theory of communication (context) - Shannon - 1984
463 Term weighting approaches in automatic text retrieval (context) - Salton, Buckley - 1988
233 The CN2 Induction algorithm - Clark, Niblett - 1989
149 Focused crawling: a new approach to topic-specific Web resou.. - Chakrabarti - 1999
139 Machine learning in automated text categorization - Sebastiani - 2002
57 Focused crawling using context graphs - Diligenti - 2000
6 Topic-driven crawlers: Machine learning issues - Menczer - 2002
4 Web classification using support vector machine - Sun, Lim et al. - 2002
3 A large benchmark dataset for web document clustering - Sinka, Corne - 2002
1 Classifying Web Pages Using Adaptive Ontology (context) - Noh - 2003
1 Backprop Package (context) - Tveter - 1996
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