DocumentCode
1985569
Title
Insensitive Modification of Subspace Information Criterion for Least Mean Squares Learning
Author
Xuejun Zhou
Author_Institution
Fac. of Math. & Comput. Sci., Huanggang Normal Univ., Huanggang, China
Volume
2
fYear
2013
fDate
28-29 Oct. 2013
Firstpage
428
Lastpage
430
Abstract
The least mean squares (LMS) algorithm is widely applied in the machine learning community. Insensitive Modification of Subspace Information Criterion (IMSIC) is one of the model selection methods, which is defined on an unbiased estimator of the generalization error-Subspace Information Criterion(SIC). In this paper, we will give the method of selecting LMS learning models by IMSIC.
Keywords
generalisation (artificial intelligence); learning (artificial intelligence); least mean squares methods; IMSIC; LMS algorithm; generalization error; insensitive modification of subspace information criterion; least mean squares algorithm; least mean squares learning; model selection methods; Computational modeling; Covariance matrices; Kernel; Least squares approximations; Noise; Silicon carbide; Training; Insensitive Modification of Subspace Information Criterion; generalization error; least mean squares algorithm; model selection;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Intelligence and Design (ISCID), 2013 Sixth International Symposium on
Conference_Location
Hangzhou
Type
conf
DOI
10.1109/ISCID.2013.219
Filename
6804918
Link To Document