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The essential
change from ordinary cascade-correlation (CC) is the elimination of any
direct input-to-output unit connection weights. If such direct connections
are retained, then learning an encoder problem is trivial, requiring only a
weight of 1.0 between each input unit and its corresponding output unit. With
such a trivial solution, a network learns nothing useful that could enable
such phenomena as pattern completion or prototype abstraction.
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