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.
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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
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