By Topic

Supervised learning with potentials for neural network-based object recognition

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Starzyk, J.A. ; Dept. of Electr. & Comput. Eng., Ohio Univ., Athens, OH, USA ; Sinkuo Chai

Supervised learning techniques are widely used in object recognition based on neural networks. Presenting class-labelled samples to the neural network and employing certain learning criteria accomplish the supervised learning process. In this research we present a learning algorithm which uses the potential function between cluster centers and samples as the learning criterion. A learning process using Euclidean distance as the criterion is also performed. Results from both methods are compared

Published in:

System Theory, 1994., Proceedings of the 26th Southeastern Symposium on

Date of Conference:

20-22 Mar 1994