Abstract
Classification is one of the most active research and application
areas of neural networks. The literature is vast and growing. This paper
summarizes some of the most important developments in neural network
classification research. Specifically, the issues of posterior
probability estimation, the link between neural and conventional
classifiers, learning and generalization tradeoff in classification, the
feature variable selection, as well as the effect of misclassification
costs are examined. Our purpose is to provide a synthesis of the
published research in this area and stimulate further research interests
and efforts in the identified topics
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