PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Semi-supervised Learning on Directed Graphs
Dengyong Zhou, Bernhard Schölkopf and Thomas Hofmann
In: NIPS 2004, Vancouver, Canada(2005).

This is the latest version of this eprint.


Given a directed graph in which some of the nodes are labeled, we investigate the question of how to exploit the link structure of the graph to infer the labels of the remaining unlabeled nodes. To that extent we propose a regularization framework for functions defined over nodes of a directed graph that forces the classification function to change slowly on densely linked subgraphs. A powerful, yet computationally simple classification algorithm is derived within the proposed framework. The experimental evaluation on real-world Web classification problems demonstrates encouraging results that validate our approach.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
Information Retrieval & Textual Information Access
ID Code:1027
Deposited By:Dengyong Zhou
Deposited On:22 July 2005

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