title: Constructing a speculative kernel machine for pattern classification.
abstract: 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.
authors.affiliation: Computational Engineering and Design Group, School of Engineering Sciences, University of Southampton, Highfield, Southampton SO17 1BJ, UK.
authors.forename: Arindam
authors.initials: A
authors.lastname: Choudhury
authors.forename: Prasanth B
authors.initials: PB
authors.lastname: Nair
authors.forename: Andy J
authors.initials: AJ
authors.lastname: Keane
mesh_terms: D000465:Algorithms; D002965:Classification / Q000379:methods; D003627:Data Interpretation, Statistical; D008959:Models, Neurological; D010363:Pattern Recognition, Automated