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Learning to Find Graph Pre-Images

2004

Conference Paper

ei


The recent development of graph kernel functions has made it possible to apply well-established machine learning methods to graphs. However, to allow for analyses that yield a graph as a result, it is necessary to solve the so-called pre-image problem: to reconstruct a graph from its feature space representation induced by the kernel. Here, we suggest a practical solution to this problem.

Author(s): BakIr, G. and Zien, A. and Tsuda, K.
Book Title: Pattern Recognition
Journal: Pattern Recognition: Proceedings of the 26th DAGM Symposium
Pages: 253-261
Year: 2004
Month: August
Day: 0
Editors: Rasmussen, C. E., H. H. B{\"u}lthoff, B. Sch{\"o}lkopf, M. A. Giese
Publisher: Springer

Department(s): Empirische Inferenz
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1007/b99676
Event Name: 26th DAGM Symposium
Event Place: Tübingen, Germany

Address: Berlin, Germany
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{2639,
  title = {Learning to Find Graph Pre-Images},
  author = {BakIr, G. and Zien, A. and Tsuda, K.},
  journal = {Pattern Recognition: Proceedings of the 26th DAGM Symposium},
  booktitle = {Pattern Recognition},
  pages = {253-261},
  editors = {Rasmussen, C. E., H. H. B{\"u}lthoff, B. Sch{\"o}lkopf, M. A. Giese},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Berlin, Germany},
  month = aug,
  year = {2004},
  month_numeric = {8}
}