Share Email Print
cover

Proceedings Paper

Automatic feature extraction in neural network noniterative learning
Author(s): Chia-Lun John Hu
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

It is proved analytically, whenever the input-output mapping of a one-layered, hard-limited perceptron satisfies a positive, linear independency (PLI) condition, the connection matrix A to meet this mapping can be obtained noniteratively in one step from an algebraic matrix equation containing an N multiplied by M input matrix U. Each column of U is a given standard pattern vector, and there are M standard patterns to be classified. It is also analytically proved that sorting out all nonsingular sub-matrices Uk in U can be used as an automatic feature extraction process in this noniterative-learning system. This paper reports the theoretical derivation and the design and experiments of a superfast-learning, optimally robust, neural network pattern recognition system utilizing this novel feature extraction process. An unedited video movie showing the speed of learning and the robustness in recognition of this novel pattern recognition system is demonstrated in life. Comparison to other neural network pattern recognition systems is discussed.

Paper Details

Date Published: 1 April 1997
PDF: 4 pages
Proc. SPIE 3030, Applications of Artificial Neural Networks in Image Processing II, (1 April 1997); doi: 10.1117/12.269775
Show Author Affiliations
Chia-Lun John Hu, Southern Illinois Univ./Carbondale (United States)


Published in SPIE Proceedings Vol. 3030:
Applications of Artificial Neural Networks in Image Processing II
Nasser M. Nasrabadi; Aggelos K. Katsaggelos, Editor(s)

© SPIE. Terms of Use
Back to Top
PREMIUM CONTENT
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?
close_icon_gray