Shultz, T. R., & Rivest, F. (2000). Knowledge-based cascade-correlation. Proceedings of the International Joint Conference on Neural Networks, Vol. V (pp. 641-646). Los Alamitos, CA: IEEE Computer Society Press.



Neural network modeling typically ignores the role of knowledge in learning by starting from random weights. A new algorithm extends cascade-correlation by recruiting previously learned networks as well as single hidden units. Knowledge-based cascade-correlation (KBCC) finds, adapts, and uses its relevant knowledge to speed learning. In this paper, we describe KBCC and illustrate its performance on a small, but clear problem.


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