A dual-phase technique for pruning constructive networks

Author

Thivierge, J.P. ; Rivest, F. ; Shultz, T.R.

Author_Institution

Dept. of Psychol., McGill Univ., Montreal, Que., Canada

Volume

1

fYear

2003

fDate

20-24 July 2003

Firstpage

559

Abstract

An algorithm for performing simultaneous growing and pruning of cascade-correlation (CC) neural networks is introduced and tested. The algorithm adds hidden units as in standard CC, and removes unimportant connections by using optimal brain damage (OBD) in both the input and output phases of CC. To this purpose, OBD was adapted to prune weights according to two separate objective functions that are used in CC to train the network, respectively. Application of the new algorithm to two databases of the PROBEN1 benchmarks reveals that this new dual-phase pruning technique is effective in significantly reducing the size of CC networks, while providing a speed-up in learning times and improvements in generalization over novel test sets.

Keywords

generalisation (artificial intelligence); learning (artificial intelligence); neural nets; PROBEN1 benchmarks; cascade-correlation neural networks; constructive networks pruning; databases; dual-phase pruning technique; input phases; optimal brain damage; output phases; Benchmark testing; Biological neural networks; Computer network management; Computer science; Databases; Network topology; Performance evaluation; Potential well; Psychology; Quality management;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2003. Proceedings of the International Joint Conference on

ISSN

1098-7576

Print_ISBN

0-7803-7898-9

Type

conf

DOI

10.1109/IJCNN.2003.1223407

Filename

1223407