DocumentCode
2950329
Title
Corrected Subspace Information Criterion for Least Mean Squares Learning
Author
Zhou, Xuejun
Author_Institution
Fac. of Math. & Comput. Sci., Huanggang Normal Univ., Huanggang, China
fYear
2011
fDate
20-21 Aug. 2011
Firstpage
408
Lastpage
410
Abstract
The least mean squares (LMS) algorithm is widely applied in the machine learning community. Corrected subspace information criterion (CSIC) 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 apply CSIC to select of LMS learning models, it can obtain better results than SIC.
Keywords
learning (artificial intelligence); least mean squares methods; corrected subspace information criterion; least mean squares learning; machine learning; Computational modeling; Covariance matrix; Kernel; Least squares approximation; Noise; Silicon carbide; Training; corrected subspace information criterion; generalization error; least mean squares algorithm; model selection;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligence Science and Information Engineering (ISIE), 2011 International Conference on
Conference_Location
Wuhan
Print_ISBN
978-1-4577-0960-9
Electronic_ISBN
978-0-7695-4480-9
Type
conf
DOI
10.1109/ISIE.2011.61
Filename
5997468
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