The Learning Dynamcis of a Universal Approximator

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

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Ansgar West, David Saad, Ian Nabney


The learning properties of a universal approximator, a normalized committee machine with adjustable biases, are studied for on-line back-propagation learning. Within a statistical mechanics frame(cid:173) work, numerical studies show that this model has features which do not exist in previously studied two-layer network models with(cid:173) out adjustable biases, e.g., attractive suboptimal symmetric phases even for realizable cases and noiseless data.