Categorization in unsupervised neural networks: the Eidos model
- 1 January 1996
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Neural Networks
- Vol. 7 (1) , 147-154
- https://doi.org/10.1109/72.478399
Abstract
Proulx and Begin (1995) recently explained the power of a learning rule that combines Hebbian and anti-Hebbian learning in unsupervised auto-associative neural networks. Combined with the brain-state-in-a-box transmission rule, this learning rule defines a new model of categorization: the Eidos model. To test this model, a simulated neural network, composed of 35 interconnected units, is subjected to an alphabetical characters recognition task. The results indicate the necessity of adding two parameters to the model: a restraining parameter and a forgetting parameter. The study shows the outstanding capacity of the model to categorize highly altered stimuli after a suitable learning process. Thus, the Eidos model seems to be an interesting option to achieve categorization in unsupervised neural networks.Keywords
This publication has 2 references indexed in Scilit:
- Self-Organization and Associative MemoryPublished by Springer Nature ,1989
- Distinctive features, categorical perception, and probability learning: Some applications of a neural model.Psychological Review, 1977