Title :
Generalization in cascade-correlation networks
Author_Institution :
Dept. of Comput. Sci., Aarhus Univ., Denmark
fDate :
31 Aug-2 Sep 1992
Abstract :
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
Keywords :
generalisation (artificial intelligence); learning (artificial intelligence); neural nets; Fahlman´s algorithm; cascade correlation learning architecture; cascade-correlation networks; generalizations; network construction algorithms; neural nets; Algorithm design and analysis; Buildings; Computer architecture; Computer networks; Computer science; Computer vision; Education; Electronic mail; Intelligent networks; Neural networks;
Conference_Titel :
Neural Networks for Signal Processing [1992] II., Proceedings of the 1992 IEEE-SP Workshop
Conference_Location :
Helsingoer
Print_ISBN :
0-7803-0557-4
DOI :
10.1109/NNSP.1992.253707
