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Proceedings Paper

Web page classification based on a binary hierarchical classifier for multi-class support vector machines
Author(s): Cunhe Li; Guangqing Wang
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Paper Abstract

Web page classification is one of the essential techniques for Web mining. This paper proposes a binary hierarchical classifier for multi-class support vector machines for web page classification. This method applies truncated singular value decomposition on the training data that reduces its dimension and the noise data. After the truncated singular value decomposition on the training data, it uses the improved k-means algorithm design the binary hierarchical structure, the improved k-means algorithm makes the separability of one macro-class is the smallest, makes the separability of two macro-classes is the largest. The result of experiment performed on the training datasets shows that this algorithm can enhance precision of web page classification.

Paper Details

Date Published: 14 March 2013
PDF: 6 pages
Proc. SPIE 8768, International Conference on Graphic and Image Processing (ICGIP 2012), 87686B (14 March 2013); doi: 10.1117/12.2014014
Show Author Affiliations
Cunhe Li, College of Computer and Communication Engineering (China)
Guangqing Wang, College of Computer and Communication Engineering (China)


Published in SPIE Proceedings Vol. 8768:
International Conference on Graphic and Image Processing (ICGIP 2012)
Zeng Zhu, Editor(s)

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