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  Transductive Inference with Graphs

Zhou, D., & Schölkopf, B.(2004). Transductive Inference with Graphs. Tübingen, Germany: Max Planck Institute for Biological Cybernetics.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-F345-1 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-8B24-9
Genre: Report

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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: We propose a general regularization framework for transductive inference. The given data are thought of as a graph, where the edges encode the pairwise relationships among data. We develop discrete analysis and geometry on graphs, and then naturally adapt the classical regularization in the continuous case to the graph situation. A new and effective algorithm is derived from this general framework, as well as an approach we developed before.

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 Dates: 2004-08
 Publication Status: Published in print
 Pages: 8
 Publishing info: Tübingen, Germany : Max Planck Institute for Biological Cybernetics
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 Identifiers: BibTex Citekey: 2828
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Title: Technical Report of the Max Planck Institute for Biological Cybernetics
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