Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
410574 | Neurocomputing | 2009 | 12 Pages |
Abstract
A unified approach is proposed for data modelling that includes supervised regression and classification applications as well as unsupervised probability density function estimation. The orthogonal-least-squares regression based on the leave-one-out test criteria is formulated within this unified data-modelling framework to construct sparse kernel models that generalise well. Examples from regression, classification and density estimation applications are used to illustrate the effectiveness of this generic data-modelling approach for constructing parsimonious kernel models with excellent generalisation capability.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
S. Chen, X. Hong, B.L. Luk, C.J. Harris,