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Proceedings Paper

Automatic feature extraction using a novel noniterative neural network
Author(s): Chia-Lun John Hu
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Paper Abstract

As we reported in the last few years, a one-layered, hard- limited perceptron is generally sufficient for carrying out a robust recognition on any untrained pattern if the training class patterns satisfy a certain PLI condition. For most pattern recognition applications, this condition should be satisfied. When this condition is satisfied, an automatic feature extraction scheme can then be derived using some N- dimension Euclidean geometry theories. This automatic scheme will automatically extract the most distinguished parts of the N-vectors used in the training. These distinguished parts or the feature vectors will then allow a very robust recognition when untrained patterns are tested in the recognition mode. Theoretical derivation and live experiments revealing the physical nature of this novel, ultra-fast learning, pattern recognition system will be presented in detail.

Paper Details

Date Published: 24 August 1999
PDF: 8 pages
Proc. SPIE 3718, Automatic Target Recognition IX, (24 August 1999); doi: 10.1117/12.359946
Show Author Affiliations
Chia-Lun John Hu, Southern Illinois Univ./Carbondale (United States)

Published in SPIE Proceedings Vol. 3718:
Automatic Target Recognition IX
Firooz A. Sadjadi, Editor(s)

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