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Meeting Abstract

Marginalized kernels between labeled graphs


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

Cite as: https://hdl.handle.net/21.11116/0000-0005-80A7-D
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.