Analysis on the Classification Error of ANNS

ANNS are efficient and objective classification methods in subject classification. It is an information processing system whose design was inspired by the structure and functioning of neuron in biology. Thus, they have been successfully applied to the numerous classification fields. Sometimes, howev...

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Bibliographic Details
Published in2009 Fifth International Joint Conference on INC, IMS and IDC pp. 1161 - 1164
Main Authors Lihua Feng, Jiahong Feng
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2009
Subjects
ANNS
Artificial neural networks
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Biological neural networks
classification
error
Humans
Information processing
Input variables
Multi-layer neural network
Multilayer perceptrons
Neural networks
Neurons
subject
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ISBN1424452090
9781424452095
DOI10.1109/NCM.2009.118

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Summary:ANNS are efficient and objective classification methods in subject classification. It is an information processing system whose design was inspired by the structure and functioning of neuron in biology. Thus, they have been successfully applied to the numerous classification fields. Sometimes, however, classifications do not match the real world, and are subjected to errors. These problems are caused by the nature of artificial neural networks. By studying of these problems, it helps us to have a better understanding on ANNS classification and find a way to improve their performance.
ISBN:1424452090
9781424452095
DOI:10.1109/NCM.2009.118