RT Journal Article
JF Parallel Algorithms / Architecture Synthesis, AIZU International Symposium on
TI Training Neocognitron to Recognize Handwritten Digits in the Real World
A1 Kunihiko Fukushima,
A1 Hayaru Shouno,
A1 Ken-ichi Nagahara,
AB Using a large scale real-world database ETL-1, we show that the 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 with the use of information of the category names of the training patterns.
PB IEEE Computer Society, [URL:http://www.computer.org]