Name
Abstract | ||
|---|---|---|
A unified approach is proposed for sparse kernel data modelling that includes regression and classification as well as probability density function estimation. The orthogonal-least-squares forward selection method based on the leave-one-out test criteria is presented 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 sparse kernel data modelling approach. |
Year | Venue | Keywords |
|---|---|---|
2007 | IDEAL | unified approach,sparse kernel model,orthogonal-least-squares forward selection method,sparse kernel data modelling,sparse kernel modelling,unified data-modelling framework,probability density function estimation,density estimation application,leave-one-out test criterion,generic sparse kernel data,data modelling,probability density function |
Field | DocType | Volume |
Radial basis function kernel,Pattern recognition,Kernel embedding of distributions,Computer science,Sparse approximation,Polynomial kernel,Artificial intelligence,Variable kernel density estimation,Kernel regression,Machine learning,Kernel (statistics),Kernel density estimation | Conference | 4881 |
ISSN | ISBN | Citations |
0302-9743 | 3-540-77225-1 | 0 |
PageRank | References | Authors |
0.34 | 13 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| S. Chen | 1 | 0 | 0.34 |
| X. Hong | 2 | 157 | 11.12 |
| C. J. Harris | 3 | 140 | 10.43 |