A fusion design of linear feedforward neural networks for pattern classification

Discusses the relationship between the mean square classifier (MSC) and the linear feedforward neural network classifier (LFNNC) and further studies the transformation properties of LFNNs under the supervision of outer-supervised signals. The obtained conclusions show that an LFNNC is equivalent to the cascade of many MSCs, and vice versa (only if the hidden units number is greater than the “bottleneck” limit). Therefore, we can cascade several MSCs to form a modular LFNNC which is completely equivalent to doing information fusion from different MSCs