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

Chen, S., Hong, X., 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

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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:Faculty of 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

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