2004 KernelsandDistancesforStructure

• (Gärtner et al., 2004) ⇒ Thomas Gärtner, John W. Lloyd, and Peter A. Flach. (2004). “Kernels and Distances for Structured Data.” In: Machine Learning Journal, 57(3). doi:10.1023/B:MACH.0000039777.23772.30

Subject Headings: Kernel Methods, Structured Data, Inductive Logic Programming, Higher-Order Logic, Instance-based Learning.

Cited By

• http://scholar.google.com/scholar?q=%222004%22+Kernels+and+Distances+for+Structured+Data
• http://dl.acm.org/citation.cfm?id=1016942.1016947&preflayout=flat#citedby

Quotes

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%.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2004 KernelsandDistancesforStructure	Thomas Gärtner Peter A. Flach John W. Lloyd			Kernels and Distances for Structured Data				10.1023/B:MACH.0000039777.23772.30		2004