ML Anthology

Kernels and Distances for Structured Data

Thomas Gärtner, John W. Lloyd, Peter A. Flach
MLJ 2004 pp. 205-232
doi:10.1023/B:MACH.0000039777.23772.30 /mlj/2004/gartner2004mlj-kernels/

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%.

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Text

Gärtner et al. "Kernels and Distances for Structured Data." Machine Learning, 2004. doi:10.1023/B:MACH.0000039777.23772.30

Markdown

[Gärtner et al. "Kernels and Distances for Structured Data." Machine Learning, 2004.](https://mlanthology.org/mlj/2004/gartner2004mlj-kernels/) doi:10.1023/B:MACH.0000039777.23772.30

BibTeX

@article{gartner2004mlj-kernels,
  title     = {{Kernels and Distances for Structured Data}},
  author    = {Gärtner, Thomas and Lloyd, John W. and Flach, Peter A.},
  journal   = {Machine Learning},
  year      = {2004},
  pages     = {205-232},
  doi       = {10.1023/B:MACH.0000039777.23772.30},
  volume    = {57},
  url       = {https://mlanthology.org/mlj/2004/gartner2004mlj-kernels/}
}