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

被引：28

作者：

Chen, S. ^{[1
]}

Hong, X. ^{[2
]}

Luk, B. L. ^{[3
]}

Harris, C. J. ^{[1
]}

机构：

[1] Univ Southampton, Sch Elect & Comp Sci, Southampton SO17 1BJ, Hants, England

[2] Univ Reading, Sch Syst Engn, Reading RG6 6AY, Berks, England

[3] City Univ Hong Kong, Dept Mfg Engn & Engn Management, Hong Kong, Hong Kong, Peoples R China

来源：

NEUROCOMPUTING | 2009年 / 72卷 / 10-12期

关键词：

Regression; Classification; Density estimation; Sparse kernel modelling; Orthogonal-least-squares algorithm; Regularisation; Leave-one-out cross-validation; Multiplicative nonnegative quadratic programming; ALGORITHM; IDENTIFICATION; PARAMETERS; SELECTION;

D O I：

10.1016/j.neucom.2008.10.002

中图分类号：

TP18 [人工智能理论];

学科分类号：

140502 [人工智能];

摘要：

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.

引用

页码：2670 / 2681

页数：12

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