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The cascade-correlation learning architecture (1990)
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| Venue: | Advances in Neural Information Processing Systems 2 |
| Citations: | 801 - 6 self |
BibTeX
@INPROCEEDINGS{Fahlman90thecascade-correlation,
author = {Scott E. Fahlman and Christian Lebiere},
title = {The cascade-correlation learning architecture},
booktitle = {Advances in Neural Information Processing Systems 2},
year = {1990},
pages = {524--532},
publisher = {Morgan Kaufmann}
}
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Abstract
Cascade-Correlation is a new architecture and supervised learning algorithm for artificial neural networks. 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 back-propagation of error signals through the connections of the network.
