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- W2005683516 abstract "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." @default.
- W2005683516 created "2016-06-24" @default.
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- W2005683516 date "2007-06-20" @default.
- W2005683516 modified "2023-10-16" @default.
- W2005683516 title "Learning from interpretations" @default.
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- W2005683516 doi "https://doi.org/10.1145/1273496.1273615" @default.
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