| Publisher | MTA SZTAKI | ||
|---|---|---|---|
| Format | 143.9KB PDF | Date added | 30 Jul 2007 |
| Topics | Artificial Intelligence, Spam - E-mail Fraud - Phishing, Learning Management Systems | ||
| Downloads | 2 | ||
This paper compares a wide range of semi-supervised learning techniques both for Web spam filtering and for telephone user churn classification. Semi-supervised learning has the assumption that the label of a node in a graph is similar to those of its neighbors. This paper measures this phenomenon both for Web spam and telco churn. They conclude that spam is often linked to spam while honest pages are linked to honest ones; similarly churn occurs in bursts in groups of a social network.
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