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  Marginalized kernels between labeled graphs

Tsuda, K. (2003). Marginalized kernels between labeled graphs. In Workshop "Kernel Methods in Bioinformatics" 2003.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0005-80A7-D Version Permalink: http://hdl.handle.net/21.11116/0000-0005-80AC-8
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 Creators:
Tsuda, K1, 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: A new kernel function between two labeled graphs is presented. Feature vectors are defined as the counts of label paths produced by random walks on graphs. The kernel computation finally boils down to obtaining the stationary state of a discrete-time linear system, thus is efficiently performed by solving simultaneous linear equations. Our kernel is based on an infinite dimensional feature space, so it is fundamentally different from other string or tree kernels based on dynamic programming. We will present promising empirical results in classification of chemical compounds.

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 Dates: 2003-04
 Publication Status: Published online
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Title: Workshop "Kernel Methods in Bioinformatics" 2003
Place of Event: Berlin, Germany
Start-/End Date: 2003-04-14

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Title: Workshop "Kernel Methods in Bioinformatics" 2003
Source Genre: Proceedings
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