DocumentCode
1800326
Title
Full design matrix designation in orthogonal least squares approximation problems
Author
Wang, Xunxian ; Brown, David
Author_Institution
Dept. of Creative Technol., Portsmouth Univ., UK
Volume
2
fYear
2004
fDate
18-20 May 2004
Firstpage
928
Abstract
Based on the forward selection formula, the relationship between the least squares cost function and the correlation between the training data and the regressors is introduced. A rule to design the full design matrix is proposed. A comparison of the experimental data shows that the method is efficient in reducing the complexity of the final approximation model.
Keywords
correlation methods; least squares approximations; regression analysis; approximation model complexity reduction; forward selection formula; full design matrix designation; kernel regression problem; least squares cost function; orthogonal least squares approximation problems; training data/regressors correlation; Approximation error; Boosting; Cost function; Equations; Intelligent systems; Kernel; Least squares approximation; Least squares methods; Support vector machines; Training data;
fLanguage
English
Publisher
ieee
Conference_Titel
Instrumentation and Measurement Technology Conference, 2004. IMTC 04. Proceedings of the 21st IEEE
ISSN
1091-5281
Print_ISBN
0-7803-8248-X
Type
conf
DOI
10.1109/IMTC.2004.1351214
Filename
1351214
Link To Document