Welke, Pascal and Horváth, Tamás and Wrobel, Stefan (2016) Probabilistic Frequent Subtree Kernels. In: Probabilistic Frequent Subtree Kernels. :. Lecture Notes in Computer Science, 9067 . Springer, Cham, Porto, pp. 179-193. ISBN 9783319393148
Full text not available from this repository.Abstract
We propose a new probabilistic graph kernel. It is defined by the set of frequent subtrees generated from a small random sample of spanning trees of the transaction graphs. In contrast to the ordinary frequent subgraph kernel it can be computed efficiently for any arbitrary graphs. Due to its probabilistic nature, the embedding function corresponding to our graph kernel is not always correct. Our empirical results on artificial and real-world chemical datasets, however, demonstrate that the graph kernel we propose is much faster than other frequent pattern based graph kernels, with only marginal loss in predictive accuracy.