Towards the Inference of Graphs on Ordered Vertexes

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