Name
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.neucom.2008.10.002 | Neurocomputing |
Keywords | DocType | Volume |
unified approach,density estimation,leave-one-out cross-validation,unified data-modelling framework,regression classification density estimation sparse kernel modelling orthogonal-least-squares algorithm regularisation leave-one-out cross-validation multiplicative nonnegative quadratic programming,classification application,unsupervised probability density function,density estimation application,parsimonious kernel model,regularisation,multiplicative nonnegative quadratic programming,orthogonal-least-squares algorithm,sparse kernel model,classification,sparse kernel modelling,regression,orthogonal-least-squares regression,data modelling,generic data-modelling approach,supervised regression,leave one out cross validation,quadratic program,probability density function | Journal | 72 |
Issue | ISSN | Citations |
10-12 | Neurocomputing | 14 |
PageRank | References | Authors |
0.61 | 29 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sheng Chen | 1 | 1035 | 111.98 |
| X. Hong | 2 | 157 | 11.12 |
| B. L. Luk | 3 | 223 | 26.27 |
| C. J. Harris | 4 | 132 | 7.59 |