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Proceedings Paper

Recognizing the untrained patterns in a noniterative perceptron learning scheme
Author(s): Chia-Lun John Hu; Jeng-Yoong Tan; Xue-Jiang Cheng; John R. Eynon; Osama Husson
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Paper Abstract

As we reported previously, a deterministic hard-limited perceptron is a novel learning system in which the learning mechanism is different from most of the conventional learning systems. It is a noniterative, one-step learning system and yet is achieves most of the goals of the conventional learning. If an optimum algorithm is adopted in the design of this learning system, the learning is very fast and the recognition is very robust. This article reports the experimental results of several computer-implemented schemes of this novel learning system. It is seen that the system takes only one to two minutes to train several given patterns and the recognition of the untrained patterns is more than 80% successful. In one scheme, the recognition rate is almost 100% and the recognition is independent of the pattern size, pattern orientation, and pattern location.

Paper Details

Date Published: 19 August 1993
PDF: 7 pages
Proc. SPIE 1966, Science of Artificial Neural Networks II, (19 August 1993); doi: 10.1117/12.152621
Show Author Affiliations
Chia-Lun John Hu, Southern Illinois Univ./Carbondale (United States)
Jeng-Yoong Tan, Southern Illinois Univ./Carbondale (United States)
Xue-Jiang Cheng, Southern Illinois Univ./Carbondale (United States)
John R. Eynon, Southern Illinois Univ./Carbondale (United States)
Osama Husson, Southern Illinois Univ./Carbondale (United States)

Published in SPIE Proceedings Vol. 1966:
Science of Artificial Neural Networks II
Dennis W. Ruck, Editor(s)

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