Kernels and Distances for Structured Data

T Gaertner, JW Lloyd, PA Flach

Research output: Contribution to journalArticle (Academic Journal)peer-review

134 Citations (Scopus)


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%.
Translated title of the contributionKernels and Distances for Structured Data
Original languageEnglish
Pages (from-to)205 - 232
Number of pages27
JournalMachine Learning
Publication statusPublished - Dec 2004


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