By Topic

Generalization in cascade-correlation networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
S. Sjogaard ; Dept. of Comput. Sci., Aarhus Univ., Denmark

Two network construction algorithms are analyzed and compared theoretically as well as empirically. The first algorithm is the cascade correlation learning architecture proposed by S. E. Fahlman (1990), while the other algorithm is a small but striking modification of the former. Fahlman's algorithm builds multilayer feedforward networks with as many layers as the number of added hidden units, while the other algorithm operates with just one layer of hidden units. This implies that their computational capabilities and the representation of the generalizations they deal with are quite diverse, and it is demonstrated how the generalization ability of the networks generated by Fahlman's algorithm is outperformed by the networks built by the new algorithm

Published in:

Neural Networks for Signal Processing [1992] II., Proceedings of the 1992 IEEE-SP Workshop

Date of Conference:

31 Aug-2 Sep 1992