Bayes statistical behavior and valid generalization of pattern classifying neural networks
- 1 July 1991
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Neural Networks
- Vol. 2 (4) , 471-475
- https://doi.org/10.1109/72.88169
Abstract
It is demonstrated both theoretically and experimentally that, under appropriate assumptions, a neural network pattern classifier implemented with a supervised learning algorithm generates the empirical Bayes rule that is optimal against the empirical distribution of the training sample. It is also shown that, for a sufficiently large sample size, asymptotic equivalence of the network-generated rule to the theoretical Bayes optimal rule against the true distribution governing the occurrence of data follows immediately from the law of large numbers. It is proposed that a Bayes statistical decision approach leads naturally to a probabilistic definition of the valid generalization which a neural network can be expected to generate from a finite training sample.Keywords
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