Data dimensionality reduction with application to simplifying RBF network structure and improving classification performance

For high dimensional data, if no preprocessing is carried out before inputting patterns to classifiers, the computation required may be too heavy. For example, the number of hidden units of a radial basis function (RBF) neural network can be too large. This is not suitable for some practical applica...

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Bibliographic Details
Main Authors: Wang, Lipo., Fu, Xiuju
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2012
Subjects:
Online Access:https://hdl.handle.net/10356/93971
http://hdl.handle.net/10220/8196
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Institution: Nanyang Technological University
Language: English