TY  - JOUR
A1  - Brause, Rüdiger W.
T1  - Self-organized learning in multi-layer networks
N2  - 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. Keywords: feedforward network layers, maximal information gain, restricted Hebbian learning, cellular neural nets, evolutionary associative learning
KW  - feedforward network layers
KW  - maximal information gain
KW  - restricted Hebbian learning
KW  - cellular neural nets
KW  - evolutionary associative learning
Y1  - 2010
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/7952
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30-79048
SN  - 0218-2130
N1  - zuerst in: International journal on artificial intelligence tools, 4.1995, S. 433-451
SP  - 1
EP  - 19
ER  -