White Papers

Semi-Supervised Learning: A Comparative Study for Web Spam and Telephone User Churn

Overview 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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Publisher
MTA SZTAKI
File Format
PDF
Date Published
Nov 29, 2008
Format
White Papers
Topics
Artificial Intelligence, Spam - E-mail Fraud - Phishing, Learning Management Systems

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