NDLI: Combinative neural-network-based classifiers for optical handwritten character and letter recognition

Content Provider	IEEE Xplore Digital Library
Author	Gao Daqi Xie Chao Nie Guiping
Copyright Year	2003
Description	Author affiliation: Dept. of Comput., East China Univ. of Sci. & Technol., Shanghai, China (Gao Daqi; Xie Chao; Nie Guiping)
Abstract	This paper compares the similarities and differences between multilayer perceptrons (MLPs) and radial basis function (RBF) neural networks, proposes the method of how to decompose a large-sample and multiple-category training set into many small-sample and two-class training subsets. Further-more, we take single-hidden-layer perceptrons and RBF networks as the basis units to construct combinative neural-network-based classifiers. This kind of combinative classifiers has higher classification accuracy and better generalization performances than their component parts. The results for recognizing the handwritten numerals and English letters show that the presented combinative classifiers are quite effective for solving the large-sample, high-dimensional and multiple-category classification problems.
Sponsorship	Int. Neural Network Soc. IEEE Neural Networks Soc
Starting Page	2232
Ending Page	2237
File Size	466960
Page Count	6
File Format	PDF
ISBN	0780378989
ISSN	10987576
DOI	10.1109/IJCNN.2003.1223757
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-07-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Character recognition Handwriting recognition Radial basis function networks Neural networks Optical network units Bayesian methods Biomedical optical imaging Chaos Computer networks Paper technology
Content Type	Text
Resource Type	Article

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