Accessibility navigation

Orthogonal-least-squares regression: A unified approach for data modelling

Lists
Tools

Chen, S., Hong, X. ORCID: https://orcid.org/0000-0002-6832-2298, Luk, B. L. and Harris, C. J. (2009) Orthogonal-least-squares regression: A unified approach for data modelling. Neurocomputing, 72 (10-12). pp. 2670-2681. ISSN 0925-2312

Full text not archived in this repository.

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.1016/j.neucom.2008.10.002

Abstract/Summary

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. (C) 2008 Elsevier B.V. All rights reserved.

Item Type:Article
Refereed:Yes
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:15174
Uncontrolled Keywords:Regression, Classification, Density estimation, Sparse kernel, modelling, Orthogonal-least-squares algorithm, Regularisation, Leave-one-out cross-validation, Multiplicative nonnegative quadratic, programming, BASIS FUNCTION NETWORKS, KERNEL DENSITY-ESTIMATION, SYSTEM-IDENTIFICATION, LOCAL REGULARIZATION, ALGORITHM, CONSTRUCTION, SELECTION, PARAMETERS, PURSUIT
Altmetric
Deposit Details

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation

See also