A comparative study of layered neural networks on misclassification in pattern recognition

When an unknown pattern is the input to a layered neural network, the neural network may classify erroneously the unknown pattern into one of training classes, depending on the similarity between an input pattern and training patterns. In this paper, four neural network models for reducing misclassification are considered, and their performance is compared with that of the basic layered network model. Each of these four models has more output units for increasing redundancy than the basic model has, while the number of units in the input layer and the number of units in the hidden layer for the five models are kept constant. In computer simulations, random patterns and mosaic face images are used to examine the performance of the five models. It is shown through the computer simulations that two models are effective in reducing misclassification