16 September 1992 Increasing classification accuracy using multiple-neural-network schemes
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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.
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George N. Bebis, George N. Bebis, Michael Georgiopoulos, Michael Georgiopoulos, George M. Papadourakis, George M. Papadourakis, Gregory L. Heileman, Gregory L. Heileman, "Increasing classification accuracy using multiple-neural-network schemes", Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); doi: 10.1117/12.140001; https://doi.org/10.1117/12.140001
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