The cascade-correlation learning architecture
Advances in neural information processing systems 2
Divide and conquer neural networks
Neural Networks
Modeling Cognitive Development on Balance Scale Phenomena
Machine Learning - Special issue on computational models of human learning
Investigation of the CasCor family of learning algorithms
Neural Networks
On-line learning in neural networks
On-line learning in neural networks
The constraint based decomposition (CBD) training architecture
Neural Networks
Generalization Abilities of Cascade Network Architecture
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Lessons in neural network training: overfitting may be harder than expected
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
The cascade-correlation learning: a projection pursuit learning perspective
IEEE Transactions on Neural Networks
Asymptotic statistical theory of overtraining and cross-validation
IEEE Transactions on Neural Networks
On the distribution of performance from multiple neural-network trials
IEEE Transactions on Neural Networks
Constructive neural-network learning algorithms for pattern classification
IEEE Transactions on Neural Networks
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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.