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An approach for constructing parsimonious generalized Gaussian kernel regression models

An approach for constructing parsimonious generalized Gaussian kernel regression models
An approach for constructing parsimonious generalized Gaussian kernel regression models
The paper proposes a novel construction algorithm for generalized Gaussian kernel regression models. Each kernel regressor in the generalized Gaussian kernel regression model has an individual diagonal covariance matrix, which is determined by maximizing the correlation between the training data and the regressor using a repeated guided random search based on boosting optimization. The standard orthogonal least squares algorithm is then used to select a sparse generalized kernel regression model from the resulting full regression matrix. Experimental results involving two real data sets demonstrate the effectiveness of the proposed regression modeling approach.
0925-2312
441-457
Wang, X.X.
38cba1ba-039f-427c-ac6f-459768df3834
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Brown, D.J.
04b8f318-c948-45c0-b542-2d75dcad8200
Wang, X.X.
38cba1ba-039f-427c-ac6f-459768df3834
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Brown, D.J.
04b8f318-c948-45c0-b542-2d75dcad8200

Wang, X.X., Chen, S. and Brown, D.J. (2004) An approach for constructing parsimonious generalized Gaussian kernel regression models. Neurocomputing, 62, 441-457.

Record type: Article

Abstract

The paper proposes a novel construction algorithm for generalized Gaussian kernel regression models. Each kernel regressor in the generalized Gaussian kernel regression model has an individual diagonal covariance matrix, which is determined by maximizing the correlation between the training data and the regressor using a repeated guided random search based on boosting optimization. The standard orthogonal least squares algorithm is then used to select a sparse generalized kernel regression model from the resulting full regression matrix. Experimental results involving two real data sets demonstrate the effectiveness of the proposed regression modeling approach.

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Published date: December 2004
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 260126
URI: https://eprints.soton.ac.uk/id/eprint/260126
ISSN: 0925-2312
PURE UUID: 0460b874-c23f-48f7-812a-f362b2b10d83

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Date deposited: 19 Nov 2004
Last modified: 19 Jul 2019 22:39

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