A fusion design of linear feedforward neural networks for pattern classification

Author

De-Shuang Huang

Author_Institution

Beijing Inst. of Syst. Eng.

Volume

4

fYear

1999

fDate

1999

Firstpage

2768

Abstract

Discusses the relationship between the mean square classifier (MSC) and the linear feedforward neural network classifier (LFNNC) and further studies the transformation properties of LFNNs under the supervision of outer-supervised signals. The obtained conclusions show that an LFNNC is equivalent to the cascade of many MSCs, and vice versa (only if the hidden units number is greater than the “bottleneck” limit). Therefore, we can cascade several MSCs to form a modular LFNNC which is completely equivalent to doing information fusion from different MSCs

Keywords

feedforward neural nets; information theory; learning (artificial intelligence); matrix algebra; pattern classification; perceptrons; fusion design; information fusion; linear feedforward neural networks; mean square classifier; outer-supervised signals; transformation properties; Data analysis; Feedforward neural networks; Linear matrix inequalities; Matrix decomposition; Merging; Neural networks; Neurons; Pattern classification; Principal component analysis; Systems engineering and theory;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1999. IJCNN '99. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-5529-6

Type

conf

DOI

10.1109/IJCNN.1999.833518

Filename

833518