Boosted ARTMAP

Author

Verzi, Stephen J. ; Heileman, Gregory L. ; Georgiopoulos, Michael ; Healy, Michael J.

Author_Institution

Dept. of Comput. Sci., New Mexico Univ., Albuquerque, NM, USA

Volume

1

fYear

1998

fDate

4-8 May 1998

Firstpage

396

Abstract

We present a modification to the fuzzy ARTMAP neural network architecture for conducting boosted learning in a probabilistic setting. We call this new architecture boosted ARTMAP (BARTMAP). Performance comparison with fuzzy ARTMAP, PROBART and ART-EMAP on some simple two-class problems is discussed. Experimental results indicate that BARTMAP gives better generalization results on some problems involving classification overlap. In addition BARTMAP requires fewer resources, i.e., network nodes, to achieve performance levels comparable to those in fuzzy ARTMAP

Keywords

ART neural nets; fuzzy neural nets; neural net architecture; pattern classification; ART-EMAP; PROBART; boosted ARTMAP; boosted learning; classification overlap; fuzzy ARTMAP neural network architecture; generalization; probabilistic learning; two-class problems; Boosting; Computational complexity; Computer architecture; Computer science; Fuzzy neural networks; Fuzzy sets; Machine learning algorithms; Neural networks; Subspace constraints; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on

Conference_Location

Anchorage, AK

ISSN

1098-7576

Print_ISBN

0-7803-4859-1

Type

conf

DOI

10.1109/IJCNN.1998.682299

Filename

682299