Neocognitron trained by winner-kill-loser with triple threshold

Authors:
Kunihiko Fukushima;Isao Hayashi;Jasmin Léveillé
Affiliations:
Fuzzy Logic Systems Institute, Iizuka, Fukuoka, Japan;Faculty of Informatics, Kansai University, Takatsuki, Osaka, Japan;Department of Cognitive and Neural Systems and Center of Excellence for Learning in Education, Science, and Technology, Boston University, Boston, MA
Venue:
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Year:
2011

Citing 2
Cited 2

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Abstract

The neocognitron is a hierarchical, multi-layered neural network capable of robust visual pattern recognition. The neocognitron acquires the ability to recognize visual patterns through learning. The winner-kill-loser is a recently introduced competitive learning rule that has been shown to improve the neocognitron's performance in character recognition. This paper proposes an improved winner-kill-loser rule, in which we use a triple threshold, instead of the dual threshold used as part of the conventional winner-kill-loser. It is shown theoretically, and also by computer simulation, that the use of a triple threshold makes the learning process more stable. In particular, a high recognition rate can be obtained with a smaller network.