Learning from Interpretations: A Rooted Kernel for Ordered Hypergraphs
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.
Cite
Text
Wachman and Khardon. "Learning from Interpretations: A Rooted Kernel for Ordered Hypergraphs." International Conference on Machine Learning, 2007. doi:10.1145/1273496.1273615Markdown
[Wachman and Khardon. "Learning from Interpretations: A Rooted Kernel for Ordered Hypergraphs." International Conference on Machine Learning, 2007.](https://mlanthology.org/icml/2007/wachman2007icml-learning/) doi:10.1145/1273496.1273615BibTeX
@inproceedings{wachman2007icml-learning,
title = {{Learning from Interpretations: A Rooted Kernel for Ordered Hypergraphs}},
author = {Wachman, Gabriel and Khardon, Roni},
booktitle = {International Conference on Machine Learning},
year = {2007},
pages = {943-950},
doi = {10.1145/1273496.1273615},
url = {https://mlanthology.org/icml/2007/wachman2007icml-learning/}
}