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.
Notes
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
;
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 |