Cascade correlation algorithm
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Cascade-Correlation is an architecture and supervised learning algorithm for artificial neural networks developed by Scott Fahlman. Instead of just adjusting the weights in a network of fixed topology, Cascade-Correlation begins with a minimal network, then automatically trains and adds new hidden units one by one, creating a multi-layer structure. Once a new hidden unit has been added to the network, its input-side weights are frozen. This unit then becomes a permanent feature-detector in the network, available for producing outputs or for creating other, more complex feature detectors. The Cascade-Correlation architecture has several advantages over existing algorithms: it learns very quickly, the network determines its own size and topology, it retains the structures it has built even if the training set changes, and it requires no backpropagation of error signals through the connections of the network.
[edit] External links
- The Cascade-Correlation Learning Architecture Scott E. Fahlman and Christian Lebiere, August 29, 1991. Article created for National Science Foundation under Contract Number EET-8716324 and Defense Advanced Research Projects Agency (DOD), ARPA Order No. 4976 under Contract F33615-87-C-1499.
