title: Bayes statistical behavior and valid generalization of pattern classifying neural networks.
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.
authors.affiliation: NTT Transmission Syst. Lab., Kanagawa.
authors.forename: F
authors.initials: F
authors.lastname: Kanaya
authors.forename: S
authors.initials: S
authors.lastname: Miyake