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Using Knowledge to Speed Learning: A Comparison of Knowledge-based Cascade-correlation and Multi-task Learning

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Using Knowledge to Speed Learning: A Comparison of Knowledge-based Cascade-correlation and Multi-task Learning
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 cascadecorrelation (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.
Thomas R. Shultz, François Rivest
Added 01 Aug 2010
Updated 01 Aug 2010
Type Conference
Year 2000
Where ICML
Authors Thomas R. Shultz, François Rivest
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