Title :
Insensitive Modification of Subspace Information Criterion for Least Mean Squares Learning
Author_Institution :
Fac. of Math. & Comput. Sci., Huanggang Normal Univ., Huanggang, China
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;
Conference_Titel :
Computational Intelligence and Design (ISCID), 2013 Sixth International Symposium on
Conference_Location :
Hangzhou
DOI :
10.1109/ISCID.2013.219
