Increasing classification accuracy using multiple neural network schemes

George N. Bebis, Michael Georgiopoulos, George M. Papadourakis, Gregory L. Heileman

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)221-231
Number of pages11
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - Sep 16 1992
Externally publishedYes
EventApplications of Artificial Neural Networks III 1992 - Orlando, United States
Duration: Apr 20 1992 → …

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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