Ultrafast learning in a hard-limited neural network pattern recognizer

Chia-Lun John Hu

doi:10.1117/12.234257

4 March 1996 Ultrafast learning in a hard-limited neural network pattern recognizer

Chia-Lun John Hu

Proceedings Volume 2664, Applications of Artificial Neural Networks in Image Processing; (1996) https://doi.org/10.1117/12.234257
Event: Electronic Imaging: Science and Technology, 1996, San Jose, CA, United States

Abstract

As we published in the last five years, the supervised learning in a hard-limited perceptron system can be accomplished in a noniterative manner if the input-output mapping to be learned satisfies a certain positive-linear-independency (or PLI) condition. When this condition is satisfied (for most practical pattern recognition applications, this condition should be satisfied,) the connection matrix required to meet this mapping can be obtained noniteratively in one step. Generally, there exist infinitively many solutions for the connection matrix when the PLI condition is satisfied. We can then select an optimum solution such that the recognition of any untrained patterns will become optimally robust in the recognition mode. The learning speed is very fast and close to real-time because the learning process is noniterative and one-step. This paper reports the theoretical analysis and the design of a practical charter recognition system for recognizing hand-written alphabets. The experimental result is recorded in real-time on an unedited video tape for demonstration purposes. It is seen from this real-time movie that the recognition of the untrained hand-written alphabets is invariant to size, location, orientation, and writing sequence, even the training is done with standard size, standard orientation, central location and standard writing sequence.

Citation Download Citation

Chia-Lun John Hu "Ultrafast learning in a hard-limited neural network pattern recognizer", Proc. SPIE 2664, Applications of Artificial Neural Networks in Image Processing, (4 March 1996); https://doi.org/10.1117/12.234257

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available