Parallel sequential induction networks: a new paradigm of neural network architecture

A scheme is presented to construct automatically a neural network architecture that takes advantage of both the parallel and sequential strategies to solve a pattern classification or decision problem. The scheme optimizes an entropy measure to train nodes that extract attributes from the training patterns. The sequential extraction of attributes with ranking order could alleviate significantly the scale-up problem of an all parallel network. Examples of decision-tree problems demonstrate amply the superior performance of this PSIN (parallel sequential induction network) against the usual backpropagation procedure in multilayered networks.<>