1990
@techreport{FaL1990,
vgclass = {report},
vgproject = {nn},
author = {Scott E. Fahlman and Christian Lebiere},
title = {The {C}ascade-{C}orrelation Learning Architecture},
number = {CMU-CS-90-100},
institution = {School of Computer Science, Carnegie Mellon University},
address = {Pittsburgh, PA},
month = {February},
year = {1990},
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.},
}