2004 KernelsandDistancesforStructure

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Subject Headings: Kernel Methods, Structured Data, Inductive Logic Programming, Higher-Order Logic, Instance-based Learning.

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Abstract

This paper brings together two strands of machine learning of increasing importance: kernel methods and highly structured data. We propose a general method for constructing a kernel following the syntactic structure of the data, as defined by its type signature in a higher-order logic. Our main theoretical result is the positive definiteness of any kernel thus defined. We report encouraging experimental results on a range of real-world data sets. By converting our kernel to a distance pseudo-metric for 1-nearest neighbour, we were able to improve the best accuracy from the literature on the Diterpene data set by more than 10%.

References

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2004 KernelsandDistancesforStructureThomas Gärtner
Peter A. Flach
John W. Lloyd
Kernels and Distances for Structured Data10.1023/B:MACH.0000039777.23772.302004