Empirische Inferenz
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. |
| Links: | |
| Number (issue): | 150 |
| Year: | 2006 |
| Month: | August |
| Day: | 0 |
| BibTeX Type: | Technical Report (techreport) |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| Institution: | Max Planck Institute for Biological Cybernetics, Tübingen |
| Language: | en |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
BibTeX
@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}
}