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Inductive Logic Programming and Kernel methods

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If you have a question about this talk, please contact David MacKay .

With the introduction of kernel methods, statistical approaches to machine learning have increasingly become the focus of research: Kernel methods routinely outperform other approaches at classification in terms of accuracy, and rest on a well-understood and long-researched statistical basis. However, each application to a problem requires the manual definition of a meaningful kernel given expert knowledge to achieve good separability. While Inductive Logic Programming (ILP) has long been established as a versatile tool for hypothesis generation given expert knowledge especially given structured data, on its own it performs poorly on noisy / numeric data. Proposals have been made and experimentally validated to combine the two approaches- using ILP to extract the features on which SVMs can then operate. This talk aims to highlight the advantages of such hybrid approaches, introduce some relevant work, and discuss open challenges / areas of subsequent research.

This talk is part of the Inference Group series.

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