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A novel learning objective function using localized generalization error bound for RBF network

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2 Author(s)
Yueng, D.S. ; Sch. of Comput. Sci. & Eng., South China Univ. of Technol., Guangzhou, China ; Chan, P.P.K.

A major issue of pattern classification problems is to train a classifier with good generalization capability. In this paper, a novel training objective function using the localized generalization error model (L-GEM) is proposed for a RBF network. The weight parameter of a RBF network is calculated to minimize its localized generalization error bound. The proposed training objective function is compared with well-known training methods: minimizing training error, Tikhonov regularization and weight decay. Experimental results show that RBF networks trained by minimizing the proposed objective function consistently outperform other methods.

Published in:

Machine Learning and Cybernetics, 2009 International Conference on  (Volume:2 )

Date of Conference:

12-15 July 2009