@inproceedings{b718789c923d4da1a80b10c2286959aa,
title = "An experimental study: on reducing RBF input dimension by ICA and PCA",
abstract = "Experimentally investigates using independent component analysis (ICA) and principle component analysis (PCA) in the reduction of the input dimension of a radial basis function (RBF) network such that the net's complexity is reduced. The results have shown that a RBF network with ICA as an input pre-process has similar generalization ability to the one without pre-processing, but the former's performance converges much faster. In contrast, a PCA based RBF leads to a deteriorated result in both convergent speed and generalization ability.",
author = "Huang, {Rong Bo} and Law, {Lap Tak} and Cheung, {Yiu Ming}",
note = "Funding information: The work described in this paper was fully supported by a Faculty Research Grant of Hong Kong Baptist University with Project Number: FRG/02–03/I-06. Publisher copyright: {\textcopyright} 2002 by the Institute of Electrical and Electronics Engineers, Inc. ; 2002 International Conference on Machine Learning and Cybernetics ; Conference date: 04-11-2002 Through 05-11-2002",
year = "2002",
month = nov,
doi = "10.1109/ICMLC.2002.1175376",
language = "English",
volume = "4",
series = "International Conference on Machine Learning and Cybernetics, ICMLC",
publisher = "IEEE",
pages = "1941--1945",
booktitle = "Proceedings of 2002 International Conference on Machine Learning and Cybernetics",
address = "United States",
}