Size of Multilayer Networks for Exact Learning: Analytic Approach

Part of Advances in Neural Information Processing Systems 9 (NIPS 1996)

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André Elisseeff, Hélène Paugam-Moisy


This article presents a new result about the size of a multilayer neural network computing real outputs for exact learning of a finite set of real samples. The architecture of the network is feedforward, with one hidden layer and several outputs. Starting from a fixed training set, we consider the network as a function of its weights. We derive, for a wide family of transfer functions, a lower and an upper bound on the number of hidden units for exact learning, given the size of the dataset and the dimensions of the input and output spaces.