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Proceedings Paper

Automatic feature extraction and feature competition in a perceptron pattern recognizer
Author(s): Chia-Lun John Hu
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Paper Abstract

When the dimension N of the input vector is much larger than the number M of different training patterns to be learned, a one-layered, hard-limited perceptron with N input nodes and P neurons (P > equals Log2M) is generally sufficient to accomplish the learning- recognition task. The recognition should be very robust and very fast if an optimum noniterative learning scheme is applied to the perceptron learning process. This paper concentrates at the discussion of two special characteristics of this novel pattern recognition system: the automatic feature extraction and the automatic feature competition. An unedited video movie recorded on a series of learning-recognition experiments may demonstrate these characteristics of the novel system in real time.

Paper Details

Date Published: 2 March 1994
PDF: 5 pages
Proc. SPIE 2243, Applications of Artificial Neural Networks V, (2 March 1994); doi: 10.1117/12.169975
Show Author Affiliations
Chia-Lun John Hu, Southern Illinois Univ./Carbondale (United States)

Published in SPIE Proceedings Vol. 2243:
Applications of Artificial Neural Networks V
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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