Shultz, T. R., & Rivest, F. (2000). Using knowledge to speed learning: A comparison of knowledge-based cascade-correlation and multi-task learning. Proceedings of the Seventeenth International Conference on Machine Learning (pp. 871-878). San Francisco: Morgan Kaufmann.
Cognitive modeling with neural networks unrealistically ignores the role of knowledge in learning by starting from random weights. It is likely that effective use of knowledge by neural networks could significantly speed learning. A new algorithm, knowledge-based cascade-correlation (KBCC), finds and adapts its relevant knowledge in new learning. Comparison to multi-task learning (MTL) reveals that KBCC uses its knowledge more effectively to learn faster.
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