Constructing a speculative kernel machine for pattern classification.
We propose and investigate the performance of a new geometry-based algorithm designed to identify potentially informative data points for classification. An incremental QR update scheme is used to build a classifier using a subset of these points as radial basis function centers. The minimum descriptive length and the leave-one-out error criteria are employed for automatic model selection. The proposed scheme is shown to generate parsimonious models, which perform generalization comparable to the state-of-the-art support and relevance vector machines.
Choudhury
Arindam
A
Computational Engineering and Design Group, School of Engineering Sciences, University of Southampton, Highfield, Southampton SO17 1BJ, UK.
Nair
Prasanth B
PB
Keane
Andy J
AJ
D000465:Algorithms; D002965:Classification / Q000379:methods; D003627:Data Interpretation, Statistical; D008959:Models, Neurological; D010363:Pattern Recognition, Automated