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ICML
2007
IEEE

Learning from interpretations: a rooted kernel for ordered hypergraphs

4 years 10 months ago
Learning from interpretations: a rooted kernel for ordered hypergraphs
The paper presents a kernel for learning from ordered hypergraphs, a formalization that captures relational data as used in Inductive Logic Programming (ILP). The kernel generalizes previous approaches to graph kernels in calculating similarity based on walks in the hypergraph. Experiments on challenging chemical datasets demonstrate that the kernel outperforms existing ILP methods, and is competitive with state-of-the-art graph kernels. The experiments also demonstrate that the encoding of graph data can affect performance dramatically, a fact that can be useful beyond kernel methods.
Gabriel Wachman, Roni Khardon
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2007
Where ICML
Authors Gabriel Wachman, Roni Khardon
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