Kernels and Distances for Structured Data

  • Authors:
  • Thomas Gä/rtner;John W. Lloyd;Peter A. Flach

  • Affiliations:
  • Fraunhofer Institut Autonome Intelligente Systeme, Germany/ Department of Computer Science, University of Bristol, United Kingdom/ Department of Computer Science III, University of Bonn, Germany. ...;Research School of Information Sciences and Engineering, The Australian National University. jwl@csl.anu.edu.au;Machine Learning, Department of Computer Science, University of Bristol, United Kingdom. peter.flach@bristol.ac.uk

  • Venue:
  • Machine Learning
  • Year:
  • 2004

Quantified Score

Hi-index 0.00

Visualization

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%.