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Proceedings Paper

Feature competition and feature extraction in a noniterative neural network pattern recognition scheme
Author(s): Chia-Lun John Hu
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Paper Abstract

As we published in the last few years, when the given input- output training vector pairs satisfy a PLI (positive-linear- independency) condition, the training and the application of a hard-limited neural network can be achieved non-iteratively with very short training time and very robust recognition when it is applied to recognize any untrained patterns. The key feature in this novel pattern recognition system is the use of slack constants in solving the connection matrix when the PLI condition is satisfied. Generally there are infinitely many ways of selecting the slack constants for meeting the training-recognition goal, but there is only one way to select them if an optimal robustness is sought in the recognition of the untrained patterns. This particular way of selecting the slack constants carries some special physical properties of the system -- the automatic feature extraction in the learning mode and the automatic feature competition in the recognition mode. Physical significance as well as mathematical analysis of these novel properties are to be explained in detail in this article. Real-time experiments are to be presented in an unedited movie. It is seen that in the system, the training of 4 hand-written characters is close to real time (less than 0.1 sec.) and the recognition of the untrained hand-written characters is greater than 90% accurate.

Paper Details

Date Published: 23 March 1998
PDF: 6 pages
Proc. SPIE 3386, Optical Pattern Recognition IX, (23 March 1998); doi: 10.1117/12.304758
Show Author Affiliations
Chia-Lun John Hu, Southern Illinois Univ./Carbondale (United States)

Published in SPIE Proceedings Vol. 3386:
Optical Pattern Recognition IX
David P. Casasent; Tien-Hsin Chao, Editor(s)

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