Pairwise Neural Network Classifiers with Probabilistic Outputs

Part of Advances in Neural Information Processing Systems 7 (NIPS 1994)

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David Price, Stefan Knerr, Léon Personnaz, Gérard Dreyfus


Multi-class classification problems can be efficiently solved by partitioning the original problem into sub-problems involving only two classes: for each pair of classes, a (potentially small) neural network is trained using only the data of these two classes. We show how to combine the outputs of the two-class neural networks in order to obtain posterior probabilities for the class decisions. The resulting probabilistic pairwise classifier is part of a handwriting recognition system which is currently applied to check reading. We present results on real world data bases and show that, from a practical point of view, these results compare favorably to other neural network approaches.