Cascading neural networks

Cascade-Correlation is an architecture and supervised
learning algorithm developed by Scott Fahlman and
Christian Lebiere. 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. See:
Cascade correlation algorithm.