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Conference Paper

A Regularization Framework for Learning from Graph Data

MPS-Authors
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Zhou,  D
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84193

Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zhou-Schoelkopf-2004.pdf
(Any fulltext), 204KB

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Citation

Zhou, D., & Schölkopf, B. (2004). A Regularization Framework for Learning from Graph Data. In ICML 2004 Workshop on Statistical Relational Learning and Its Connections to Other Fields (SRL 2004) (pp. 132-137).


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-F3AB-E
Abstract
The data in many real-world problems can be thought of as a graph, such as the web, co-author networks, and biological networks. We propose a general regularization framework on graphs, which is applicable to the classification, ranking, and link prediction problems. We also show that the method can be explained as lazy random walks. We evaluate the method on a number of experiments.