@inproceedings{2639,
  title = {Learning to Find Graph Pre-Images},
  journal = {Pattern Recognition: Proceedings of the 26th DAGM Symposium},
  booktitle = {Pattern Recognition},
  abstract = {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.},
  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},
  author = {BakIr, G. and Zien, A. and Tsuda, K.},
  doi = {10.1007/b99676},
  month_numeric = {8}
}