By Topic

Training neocognitron to recognize handwritten digits in the real world

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

3 Author(s)
Fukushima, K. ; Fac. of Eng. Sci., Osaka Univ., Japan ; Nagahara, K. ; Shouno, H.

Using a large-scale real-world database-the ETL-1 database of the Electrotechnical Laboratory in Japan-we show that a neocognitron trained by unsupervised learning with a winner-take-all process can recognize handwritten digits with a recognition rate higher than 97%. We use the technique of dual thresholds for feature-extracting S-cells, and higher threshold values are used in the learning than in the recognition phase. We discuss how the threshold values affect the recognition rate. The learning method for the cells of the highest stage of the network has been modified from the conventional one, in order to reconcile the unsupervised learning procedure with the use of information about the category names of the training patterns

Published in:

Parallel Algorithms/Architecture Synthesis, 1997. Proceedings., Second Aizu International Symposium

Date of Conference:

17-21 Mar 1997