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  A Regularization Framework for Learning from Graph Data

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).

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-F3AB-E Version Permalink: http://hdl.handle.net/21.11116/0000-0005-52D8-B
Genre: Conference Paper

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Zhou-Schoelkopf-2004.pdf (Any fulltext), 204KB
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 Creators:
Zhou, D1, 2, Author              
Schölkopf, B1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 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.

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 Dates: 2004-07
 Publication Status: Published in print
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 Identifiers: BibTex Citekey: 2688
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Title: ICML 2004 Workshop on Statistical Relational Learning and Its Connections to Other Fields (SRL 2004)
Place of Event: Banff, Canada
Start-/End Date: 2004-07-08

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Title: ICML 2004 Workshop on Statistical Relational Learning and Its Connections to Other Fields (SRL 2004)
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 132 - 137 Identifier: -