Affiliation:
1. J.W. Goethe-University, Computer Science Dep., NIPS, D-60054 Frankfurt., Germany
Abstract
We present a framework for the self-organized formation of high level learning by a statistical preprocessing of features. The paper focuses first on the formation of the features in the context of layers of feature processing units as a kind of resource-restricted associative multiresolution learning We clame that such an architecture must reach maturity by basic statistical proportions, optimizing the information processing capabilities of each layer. The final symbolic output is learned by pure association of features of different levels and kind of sensorial input. Finally, we also show that common error-correction learning for motor skills can be accomplished also by non-specific associative learning.
Publisher
World Scientific Pub Co Pte Lt
Subject
Artificial Intelligence,Artificial Intelligence
Cited by
2 articles.
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