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Learning to Find Graph Pre-images

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 3175)

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.

Keywords

  • 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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© 2004 Springer-Verlag Berlin Heidelberg

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Bakır, G.H., Zien, A., Tsuda, K. (2004). Learning to Find Graph Pre-images. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds) Pattern Recognition. DAGM 2004. Lecture Notes in Computer Science, vol 3175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28649-3_31

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  • DOI: https://doi.org/10.1007/978-3-540-28649-3_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22945-2

  • Online ISBN: 978-3-540-28649-3

  • eBook Packages: Springer Book Archive