1991
@techreport{Fah1991,
vgclass = {report},
vgproject = {nn},
author = {Scott E. Fahlman},
title = {The Recurrent {C}ascade-{C}orrelation Architecture},
number = {CMU-CS-91-100},
institution = {School of Computer Science, Carnegie Mellon University},
address = {Pittsburgh, PA 15213},
month = {May},
year = {1991},
abstract = {Recurrent Cascade-Correlation (RCC) is a recurrent version
of the Cascade-Correlation learning architecture of Fahlman and
Lebiere. 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 behaviours 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.},
}