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Towards the Inference of Graphs on Ordered Vertexes

MPS-Authors
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Zien,  A
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Friedrich Miescher Laboratory, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Rätsch,  G
Friedrich Miescher Laboratory, Max Planck Society;

/persons/resource/persons84118

Ong,  CS
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Friedrich Miescher Laboratory, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Fulltext (public)

MPIK-TR-150.pdf
(Publisher version), 168KB

Supplementary Material (public)
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Citation

Zien, A., Rätsch, G., & Ong, C.(2006). Towards the Inference of Graphs on Ordered Vertexes (150). Tübingen, Germany: Max Planck Institute for Biological Cybernetics.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-D09D-A
Abstract
We propose novel methods for machine learning of structured output spaces. Specifically, we consider outputs which are graphs with vertices that have a natural order. We consider the usual adjacency matrix representation of graphs, as well as two other representations for such a graph: (a) decomposing the graph into a set of paths, (b) converting the graph into a single sequence of nodes with labeled edges. For each of the three representations, we propose an encoding and decoding scheme. We also propose an evaluation measure for comparing two graphs.