title: Link Based Small Sample Learning for Web Spam Detection
creator: Geng, Guang-Gang
creator: Li, Qiudan
creator: Zhang, Xinchang
description: Robust statistical learning based web spam detection sys-  tem often requires large amounts of labeled training data.  However, labeled samples are more difficult, expensive and  time consuming to obtain than unlabeled ones. This pa-  per proposed link based semi-supervised learning algorithms  to boost the performance of a classifier, which integrates  the traditional Self-training with the topological dependency  based link learning. The experiments with a few labeled  samples on standard WEBSPAM-UK2006 benchmark showed  that the algorithms are effective.  
date: 2009-04
type: Conference or Workshop Item
type: PeerReviewed
format: application/pdf
identifier: http://www2009.eprints.org/173/1/p1185.pdf
identifier: Geng, Guang-Gang <http://www2009.eprints.org/view/author/Geng=3AGuang-Gang=3A=3A.html> and Li, Qiudan <http://www2009.eprints.org/view/author/Li=3AQiudan=3A=3A.html> and Zhang, Xinchang <http://www2009.eprints.org/view/author/Zhang=3AXinchang=3A=3A.html> (2009) Link Based Small Sample Learning for Web Spam Detection. In: 18th International World Wide Web Conference, April 20th-24th, 2009, Madrid, Spain.
relation: http://www2009.eprints.org/173/