Name
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. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/978-3-540-28649-3_31 | PATTERN RECOGNITION |
Keywords | Field | DocType |
machine learning,feature space,kernel function | Graph kernel,Strength of a graph,Graph property,Algorithm,Null graph,Lattice graph,Voltage graph,Mathematics,Graph (abstract data type),Complement graph | Conference |
Volume | ISSN | Citations |
3175 | 0302-9743 | 13 |
PageRank | References | Authors |
0.68 | 5 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gökhan H. Bakir | 1 | 228 | 14.66 |
| Alexander Zien | 2 | 1255 | 146.93 |
| Koji Tsuda | 3 | 1664 | 122.25 |
| rasmussen | 4 | 25 | 2.41 |
| c e | 5 | 15 | 1.12 |
| Heinrich H. Bülthoff | 6 | 2524 | 384.40 |
| Bernhard Schölkopf | 7 | 23120 | 3091.82 |
| Martin A. Giese | 8 | 498 | 57.43 |