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Learning from Interpretations: A Rooted Kernel for Ordered Hypergraphs

Learning from Interpretations: A Rooted Kernel for Ordered Hypergraphs

This video was recorded at 24th Annual International Conference on Machine Learning (ICML), Corvallis 2007. 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.

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