Title

Increasing Classification Accuracy Using Multiple Neural Network Schemes

Abstract

Back propagation neural networks have been widely used as classifiers in many complex classification tasks. However, early experimental results show that as the number of classes involved in a classification task increases, the classification accuracy of these networks decreases, especially in the presence of noisy inputs. In addition, larger size networks are needed to be utilized in such cases, a fact that may not always be possible. In order to overcome both of these undesirable effects a new approach is proposed in this paper which utilizes multiple, relatively small size networks to perform the classification task. This approach has been applied on a machine printed character recognition experiment and it has demonstrated better classification accuracy than the one exhibited by the single, larger size, network approach.

Publication Date

9-16-1992

Publication Title

Proceedings of SPIE - The International Society for Optical Engineering

Volume

1709

Number of Pages

221-231

Document Type

Article; Proceedings Paper

Identifier

scopus

Personal Identifier

scopus

DOI Link

https://doi.org/10.1117/12.140001

Socpus ID

85007214278 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85007214278

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