TY  - CONF
ID  - www2009173
UR  - http://www2009.eprints.org/173/
A1  - Geng, Guang-Gang
A1  - Li, Qiudan
A1  - Zhang, Xinchang
Y1  - 2009/04//
N2  - 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.

TI  - Link Based Small Sample Learning for Web Spam Detection
SP  - 1185
M2  - Madrid, Spain
AV  - public
EP  - 1185
T2  - 18th International World Wide Web Conference
ER  -