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- Title
Kernels and Distances for Structured Data.
- Authors
Thomas Gärtner; John W. Lloyd; Peter A. Flach
- 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%.
- Publication
Machine Learning, 2004, Vol 57, Issue 3, p205
- ISSN
0885-6125
- Publication type
Article
- DOI
10.1023/B:MACH.0000039777.23772.30