Shultz, T. R., Rivest, F., Egri, L., & Thivierge, J. P. (2006). Knowledge-based learning with KBCC. Paper presented at the International Conference on Development and Learning, Indiana University, Bloomington, IN.



A constructive learning algorithm, knowledge-based cascade-correlation (KBCC), recruits previously-learned networks in addition to the single hidden units recruited by ordinary cascade-correlation. This enables learning by analogy when adequate prior knowledge is available, learning by induction from examples when there is no relevant prior knowledge, and various combinations of analogy and induction. A review of experiments with KBCC indicates that recruitment of relevant existing knowledge typically speeds learning and sometimes enables learning of otherwise impossible problems. Current limitations of this approach are discussed.


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