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Cascade-correlation (cascor) networks grow by recruiting hidden units to adjust their computational power to the task being learned. The standard cascor algorithm recruits each hidden unit on a new layer, creating deep networks. In contrast, the flat cascor variant adds all recruited hidden units on a single hidden layer. Student-teacher network approximation tasks were used to investigate the ability of flat and standard cascor networks to learn the input-output mapping of other, randomly initialized flat and standard cascor networks. For low-complexity approximation tasks, there was no significant performance difference between flat and standard student networks. Contrary to the common belief that standard cascor does not generalize well due to cascading weights creating deep networks, we found that both standard and flat cascor generalized well on problems of varying complexity. On high-complexity tasks, flat cascor networks had fewer connection weights and learned with less computational cost than standard networks did.

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