Encyclopedia of Machine Learning

2010 Edition
| Editors: Claude Sammut, Geoffrey I. Webb

Cascade-Correlation

  • Thomas R. Shultz
  • Scott E. Fahlman
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-30164-8_92

Synonyms

 Cascor;  CC

Definition

Cascade-Correlation (often abbreviated as “Cascor” or “CC”) is a  supervised learning algorithm for  artificial neural networks. It is related to the  back-propagation algorithm (“backprop”). CC differs from backprop in that a CC network begins with no hidden units, and then adds units one-by-one, as needed during learning.

Each new hidden unit is trained to correlate with residual error in the network built so far. When it is added to the network, the new unit is frozen, in the sense that its input weights are fixed. The hidden units form a cascade: each new unit receives weighted input from all the original network inputs and from the output of every previously created hidden unit. This cascading creates a network that is as deep as the number of hidden units. Stated another way, the CC algorithm is capable of efficiently creating complex, higher-order nonlinear basis functions – the hidden units – which are then combined to form the desired outputs.

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Recommended Reading

  1. Baluja, S., & Fahlman, S. E. (1994). Reducing network depth in the cascade-correlation learning architecture. Pittsburgh, PA: School of Computer Science, Carnegie Mellon University.Google Scholar
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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Thomas R. Shultz
  • Scott E. Fahlman

There are no affiliations available