Constructing a speculative kernel machine for pattern classification

✍ Scribed by Arindam Choudhury; Prasanth B. Nair; Andy J. Keane

• Book ID: 104065344
• Publisher: Elsevier Science
• Year: 2006
• Tongue: English
• Weight: 153 KB
• Volume: 19
• Category: Article
• ISSN: 0893-6080
• DOI: 10.1016/j.neunet.2005.06.051

✦ Synopsis

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.