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Kernel-based data modelling using orthogonal least squares selection with local regularisation

Kernel-based data modelling using orthogonal least squares selection with local regularisation
Kernel-based data modelling using orthogonal least squares selection with local regularisation
Combining orthogonal least squares (OLS) model selection with local regularisation or smoothing leads to efficient sparse kernel-based data modelling. By assigning each orthogonal weight in the regression model with an individual regularisation parameter, the ability for the OLS model selection to produce a very parsimonious model with excellent generalisation performance is greatly enhanced.
0 9533890 4 9
27-30
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53

Chen, S. (2001) Kernel-based data modelling using orthogonal least squares selection with local regularisation. 7th Annual Chinese Automation and Computer Science Conference in UK. pp. 27-30 .

Record type: Conference or Workshop Item (Other)

Abstract

Combining orthogonal least squares (OLS) model selection with local regularisation or smoothing leads to efficient sparse kernel-based data modelling. By assigning each orthogonal weight in the regression model with an individual regularisation parameter, the ability for the OLS model selection to produce a very parsimonious model with excellent generalisation performance is greatly enhanced.

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More information

Published date: September 2001
Additional Information: Presented at 7th Annual Chinese Automation and Computer Science Conference in UK (Nottingham) , Sept.22, 2001
Venue - Dates: 7th Annual Chinese Automation and Computer Science Conference in UK, 2001-09-01
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 255977
URI: https://eprints.soton.ac.uk/id/eprint/255977
ISBN: 0 9533890 4 9
PURE UUID: e3d0af4b-9daa-4086-aa25-2dc664d4f9b0

Catalogue record

Date deposited: 24 Sep 2001
Last modified: 18 Jul 2017 09:49

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