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Towards the Inference of Graphs on Ordered Vertexes

2006

Technical Report

ei


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.

Author(s): Zien, A. and Raetsch, G. and Ong, CS.
Number (issue): 150
Year: 2006
Month: August
Day: 0

Department(s): Empirische Inferenz
Bibtex Type: Technical Report (techreport)

Institution: Max Planck Institute for Biological Cybernetics, Tübingen

Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF

BibTex

@techreport{4133,
  title = {Towards the Inference of Graphs on Ordered Vertexes},
  author = {Zien, A. and Raetsch, G. and Ong, CS.},
  number = {150},
  organization = {Max-Planck-Gesellschaft},
  institution = {Max Planck Institute for Biological Cybernetics, Tübingen},
  school = {Biologische Kybernetik},
  month = aug,
  year = {2006},
  month_numeric = {8}
}