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. |
| Links: | |
| 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 |
| BibTeX Type: | Conference Paper (inproceedings) |
| Address: | Berlin, Germany |
| DOI: | 10.1007/b99676 |
| Event Name: | 26th DAGM Symposium |
| Event Place: | Tübingen, Germany |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| Language: | en |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
BibTeX
@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}
}