DocumentCode
3347341
Title
ε-Insensitive Modification of Subspace Information Criterion
Author
Zhou, Xuejun
Author_Institution
Fac. of Math. & Inf. Sci., Huanggang Normal Univ., Huanggang, China
fYear
2009
fDate
14-17 Oct. 2009
Firstpage
188
Lastpage
191
Abstract
Evaluating the generalization performance of learning machines without using additional test samples is one of the most important issues in the machine learning community. The subspace information criterion (SIC) is one of the methods for this purpose, which is shown to be an unbiased estimator of the generalization error with finite samples. In this paper, we give ε-insensitive modification of the subspace information criterion (mSIC), it can improve the precision of SIC.
Keywords
generalisation (artificial intelligence); learning (artificial intelligence); ε-insensitive modification; generalization error; learning machine; performance evaluation; subspace information criterion; Degradation; Function approximation; Genetics; Information science; Kernel; Machine learning; Mathematics; Parameter estimation; Silicon carbide; Testing; generalization error; insensitive modification; precision; subspace information criterion;
fLanguage
English
Publisher
ieee
Conference_Titel
Genetic and Evolutionary Computing, 2009. WGEC '09. 3rd International Conference on
Conference_Location
Guilin
Print_ISBN
978-0-7695-3899-0
Type
conf
DOI
10.1109/WGEC.2009.60
Filename
5402914
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