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Orthogonal least squares regression: An efficient approach for parsimonious modelling from large data

Orthogonal least squares regression: An efficient approach for parsimonious modelling from large data
Orthogonal least squares regression: An efficient approach for parsimonious modelling from large data
The orthogonal least squares (OLS) algorithm, developed in the late 1980s for nonlinear system modelling, remains highly popular for nonlinear data modelling practicians, for the reason that the algorithm is simple and efficient, and is capable of producing parsimonious nonlinear models with good generalisation performance. Since its derivation, many enhanced variants of the OLS forward regression have been developed by incorporating the recent developments from machine learning. Notably, regularisation techniques, optimal experimental design methods and leave-one-out cross validation have been combined with the OLS algorithm. The resultant class of OLS algorithms offers the state-of-the-arts for parsimonious modelling from large data.Other topics discussed in this talk include effective grey-box modelling by incorporating the prior knowledge naturally to the model structure, and further efficiency enhancement for the OLS forward regression modelling by implementing the branch and bound strategy. Some very recent extensions of this unified data modelling approach will also be briefly presented.
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80

(2011) Orthogonal least squares regression: An efficient approach for parsimonious modelling from large data. 11th UK Workshop on Computational Intelligence, United Kingdom. 07 - 09 Sep 2011.

Record type: Conference or Workshop Item (Other)

Abstract

The orthogonal least squares (OLS) algorithm, developed in the late 1980s for nonlinear system modelling, remains highly popular for nonlinear data modelling practicians, for the reason that the algorithm is simple and efficient, and is capable of producing parsimonious nonlinear models with good generalisation performance. Since its derivation, many enhanced variants of the OLS forward regression have been developed by incorporating the recent developments from machine learning. Notably, regularisation techniques, optimal experimental design methods and leave-one-out cross validation have been combined with the OLS algorithm. The resultant class of OLS algorithms offers the state-of-the-arts for parsimonious modelling from large data.Other topics discussed in this talk include effective grey-box modelling by incorporating the prior knowledge naturally to the model structure, and further efficiency enhancement for the OLS forward regression modelling by implementing the branch and bound strategy. Some very recent extensions of this unified data modelling approach will also be briefly presented.

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More information

Published date: 27 September 2011
Additional Information: Keynote Speech Event Dates: September 7-9, 2011
Venue - Dates: 11th UK Workshop on Computational Intelligence, United Kingdom, 2011-09-07 - 2011-09-09
Organisations: Southampton Wireless Group

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Local EPrints ID: 272646
URI: http://eprints.soton.ac.uk/id/eprint/272646
PURE UUID: e51b8ced-f1e0-4979-adc4-178aefddb076

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Date deposited: 08 Aug 2011 12:45
Last modified: 18 Jul 2017 06:22

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