DocumentCode
2256854
Title
Feature weighting based on L-GEM
Author
Wang, Qian-cheng ; Ng, Wing W Y ; Chan, Patrick P K ; Yeung, Daniel S.
Author_Institution
Machine Learning & Cybern. Res. Center, South China Univ. of Technol., Guangzhou, China
Volume
1
fYear
2010
fDate
11-14 July 2010
Firstpage
220
Lastpage
224
Abstract
In this paper, we propose a novel method to weight features for their relevance to the given classification problem. The weight of a feature is computed by its Localized Generalization Error model (L-GEM). Then, a Radial Basis Function Neural Network (RBFNN) is trained by those weighted features. Experimental results on image classification problem show that the proposed method is efficient and effective in comparison to current methods.
Keywords
image classification; radial basis function networks; L-GEM; RBFNN; classification problem; feature weighting method; image classification problem; localized generalization error model; radial basis function neural network; Cybernetics; Image classification; Image color analysis; Machine learning; Neurons; Training; Transform coding; Feature weighting; Image classification; Localized Generalization Error Model; RBFNN;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics (ICMLC), 2010 International Conference on
Conference_Location
Qingdao
Print_ISBN
978-1-4244-6526-2
Type
conf
DOI
10.1109/ICMLC.2010.5581062
Filename
5581062
Link To Document