A CRITICAL COMPARISON OF TWO KINDS OF ADAPTIVE CLASSIFICATION NETWORKS.
STANFORD UNIV CA STANFORD ELECTRONICS LABS
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This report compares the capabilities and equipment requirements of two kinds of adaptive classification networks--the Learning Matric, and the Madaline. The Learning Matrix and the Madaline are structurally quite similar, forming arrays of binary output signals from linear combinations of binary or analog input signals. Both systems can learn adjust their own parameters from sets of patterns with corresponding desired outputs presented to them, and they can thereafter successfully classify additional patterns as presented. The two systems differ primarily in their training methods and in their output logic. Both the Learning Matrix and the Madaline can be realized either by the use of adaptive hardware or by digital-computer simulation. The comparison indicates that in most cases the Learning Matrix will require fewer adaptation cycles than will the Madaline when applied to a given problem on the other hand, the Madaline will usually require less equipment than the Learning Matrix. However, in making a choice of techniques for a given situation, the overriding consideration will usually be the question of optimality. Author