Abstract
A new constructive learning algorithm to generate binary neural networks is presented. The networks that are constructed are layered feedforward neural networks. Each layer is constructed to reduce the number of internal representations under the constraint that it is faithful. This algorithm, the Patch algorithm, is able to handle analogue inputs and problems with multiple output states. The algorithm allows training sets up to several thousands of patterns with up to several thousands of coordinates per pattern.
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