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Orthogonal least squares regression with tunable kernels

Orthogonal least squares regression with tunable kernels
Orthogonal least squares regression with tunable kernels
A novel technique is proposed to construct sparse regression models based on the orthogonal least squares method with tunable kernels. The proposed technique tunes the centre vector and diagonal covariance matrix of individual regressor by incrementally minimising the training mean square error using a guided random search algorithm, and it offers a state-of-the-art method for constructing very sparse models that generalise well.
0013-5194
484-486
Chen, S.
9310a111-f79a-48b8-98c7-383ca93cbb80
Wang, X.X.
38cba1ba-039f-427c-ac6f-459768df3834
Brown, D.J.
04b8f318-c948-45c0-b542-2d75dcad8200
Chen, S.
9310a111-f79a-48b8-98c7-383ca93cbb80
Wang, X.X.
38cba1ba-039f-427c-ac6f-459768df3834
Brown, D.J.
04b8f318-c948-45c0-b542-2d75dcad8200

Chen, S., Wang, X.X. and Brown, D.J. (2005) Orthogonal least squares regression with tunable kernels. Electronics Letters, 41 (8), 484-486.

Record type: Article

Abstract

A novel technique is proposed to construct sparse regression models based on the orthogonal least squares method with tunable kernels. The proposed technique tunes the centre vector and diagonal covariance matrix of individual regressor by incrementally minimising the training mean square error using a guided random search algorithm, and it offers a state-of-the-art method for constructing very sparse models that generalise well.

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Published date: April 2005
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 260780
URI: http://eprints.soton.ac.uk/id/eprint/260780
ISSN: 0013-5194
PURE UUID: 4426424b-69ef-473a-b5a0-899d6105ef6c

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Date deposited: 19 Apr 2005
Last modified: 18 Jan 2022 17:54

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Contributors

Author: S. Chen
Author: X.X. Wang
Author: D.J. Brown

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