Pairwise Neural Network Classifiers with Probabilistic Outputs
Abstract
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.
Cite
Text
Price et al. "Pairwise Neural Network Classifiers with Probabilistic Outputs." Neural Information Processing Systems, 1994.Markdown
[Price et al. "Pairwise Neural Network Classifiers with Probabilistic Outputs." Neural Information Processing Systems, 1994.](https://mlanthology.org/neurips/1994/price1994neurips-pairwise/)BibTeX
@inproceedings{price1994neurips-pairwise,
title = {{Pairwise Neural Network Classifiers with Probabilistic Outputs}},
author = {Price, David and Knerr, Stefan and Personnaz, Léon and Dreyfus, Gérard},
booktitle = {Neural Information Processing Systems},
year = {1994},
pages = {1109-1116},
url = {https://mlanthology.org/neurips/1994/price1994neurips-pairwise/}
}