The Recurrent Cascade-Correlation Architecture (1990)
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| Venue: | ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 3 |
| Citations: | 67 - 0 self |
BibTeX
@INPROCEEDINGS{Fahlman90therecurrent,
author = {Scott E. Fahlman},
title = {The Recurrent Cascade-Correlation Architecture},
booktitle = {ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 3},
year = {1990},
pages = {190--196},
publisher = {Morgan Kaufmann Publishers Inc.}
}
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Abstract
Recurrent Cascade-Correlation (RCC) is a recurrent version of the Cascade-Correlation learning architecture of Fahlman and Lebiere [Fahlman, 1990]. RCC can learn from examples to map a sequence of inputs into a desired sequence of outputs. New hidden units with recurrent connections are added to the network one at a time, as they are needed during training. In effect, the network builds up a finite-state machine tailored specifically for the current problem. RCC retains the advantages of Cascade-Correlation: fast learning, good generalization, automatic construction of a near-minimal multi-layered network, and the ability to learn complex behaviors through a sequence of simple lessons. The power of RCC is demonstrated on two tasks: learning a finite-state grammar from examples of legal strings, and learning to recognize characters in Morse code.
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