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Kernels and distances for structured data
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DOI：10.1023/B:MACH.0000039777.23772.30.png)
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Abstract
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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%.
Keywords:
kernel methods
structured data
inductive logic programming
higher-order logic
instance-based learning
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