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キーワード:
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要旨:
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.
