Orthogonal Least Square with Boosting for Regression
Orthogonal Least Square with Boosting for Regression
A novel technique is presented to construct sparse regression models based on the orthogonal least square method with boosting. This technique tunes the mean vector and diagonal covariance matrix of individual regressor by incrementally minimizing the training mean square error. A weighted optimization method is developed based on boosting to append regressors one by one in an orthogonal forward selection procedure. Experimental results obtained using this technique demonstrate that it offers a viable alternative to the existing state-of-art kernel modeling methods for constructing parsimonious regression models.
3-540-22881-0
593-599
Chen, S.
9310a111-f79a-48b8-98c7-383ca93cbb80
Wang, X.X.
38cba1ba-039f-427c-ac6f-459768df3834
Brown, D.J.
04b8f318-c948-45c0-b542-2d75dcad8200
Yang, Z.R.
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Everson, R.
229c6ebc-01dd-412c-9f65-3d0df6417655
Yin, H.J.
f26dcb60-0113-43f0-ae3c-f06cda840d54
2004
Chen, S.
9310a111-f79a-48b8-98c7-383ca93cbb80
Wang, X.X.
38cba1ba-039f-427c-ac6f-459768df3834
Brown, D.J.
04b8f318-c948-45c0-b542-2d75dcad8200
Yang, Z.R.
b71f8582-83df-4c36-9925-136cdfd207f9
Everson, R.
229c6ebc-01dd-412c-9f65-3d0df6417655
Yin, H.J.
f26dcb60-0113-43f0-ae3c-f06cda840d54
Chen, S., Wang, X.X. and Brown, D.J.
(2004)
Orthogonal Least Square with Boosting for Regression.
Yang, Z.R., Everson, R. and Yin, H.J.
(eds.)
5th International Conference on Data Engineering and Automated Learning, Exeter, United Kingdom.
25 - 27 Aug 2004.
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
A novel technique is presented to construct sparse regression models based on the orthogonal least square method with boosting. This technique tunes the mean vector and diagonal covariance matrix of individual regressor by incrementally minimizing the training mean square error. A weighted optimization method is developed based on boosting to append regressors one by one in an orthogonal forward selection procedure. Experimental results obtained using this technique demonstrate that it offers a viable alternative to the existing state-of-art kernel modeling methods for constructing parsimonious regression models.
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Published date: 2004
Additional Information:
Springer LNCS 3177 The paper received the best paper award. Event Dates: August 25-27, 2004
Venue - Dates:
5th International Conference on Data Engineering and Automated Learning, Exeter, United Kingdom, 2004-08-25 - 2004-08-27
Organisations:
Southampton Wireless Group
Identifiers
Local EPrints ID: 259859
URI: http://eprints.soton.ac.uk/id/eprint/259859
ISBN: 3-540-22881-0
PURE UUID: 1e4e0eac-2108-4023-9b8e-0e4f4778fb63
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Date deposited: 30 Aug 2004
Last modified: 14 Mar 2024 06:29
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Contributors
Author:
S. Chen
Author:
X.X. Wang
Author:
D.J. Brown
Editor:
Z.R. Yang
Editor:
R. Everson
Editor:
H.J. Yin
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