Learning to Find Graph Pre-images

  • Gökhan H. Bakır
  • Alexander Zien
  • Koji Tsuda
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3175)


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.


Adjacency Matrix Undirected Graph Input Space Nitro Compound Reproduce Kernel Hilbert Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Gökhan H. Bakır
    • 1
  • Alexander Zien
    • 1
  • Koji Tsuda
    • 1
    • 2
  1. 1.Max Planck Institute for Biological Cybernetics, Dept. SchölkopfTübingenGermany
  2. 2.AIST Computational Biology Research CenterTokyoJapan

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