NDLI: Fuzzily modular single-layer RBF neural networks for solving large-scale classification problems

Content Provider	IEEE Xplore Digital Library
Author	Gao Daqi Tong Zhen
Copyright Year	2005
Description	Author affiliation: Dept. of Comput. Sci., East China Univ. of Sci. & Technol., Shanghai (Gao Daqi; Tong Zhen)
Abstract	This paper presents a type of combinative and modular single-layer radial basis function (RBF) neural network classifiers for solving the large-scale learning problems. We pay attention simultaneously to large samples, high dimensionality and multiple categories, not to only one or two terms among them. Above all, we divide a large-scale learning problem into multiple limited-scale simple problems. Each learning subset only includes a small part of samples from the original learning set. And furthermore, we propose a kind of modular single-layer RBF classifiers, in which each module is made up of multiple RBF kernels. The optimization method for determining the number, locations, widths and target values of RBF kernels is gone into details. The real output of one module is the sum of its components, and the class label of a certain sample is finally determined by the module with the maximum output. An application example, i.e., letter recognition, shows that the proposed RBF network is quite effective for solving the large-scale learning problems
Sponsorship	IEEE IEEE Neural Networks Soc
Starting Page	1080
Ending Page	1085
File Size	1929770
Page Count	6
File Format	PDF
ISBN	0780391594
DOI	10.1109/FUZZY.2005.1452545
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-05-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Large-scale systems Kernel Computer science Laboratories Bioreactors Optimization methods Radial basis function networks Shape Power generation economics
Content Type	Text
Resource Type	Article

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