DocumentCode :
274161
Title :
A non-competitive model for unsupervised learning
Author :
Hrycej, T.
fYear :
1989
fDate :
16-18 Oct 1989
Firstpage :
233
Lastpage :
237
Abstract :
The learning algorithm presented is noncompetitive, it is related to the principal component analysis rather than to cluster analysis. It is based on `backward inhibition´, i.e., the inhibition of features already discovered in the input, which makes finding further, more subtle features possible. It is shown that the backward-inhibition algorithm is superior to the competitive feature discovery algorithm in feature independence and controllable grain. Moreover, the representation in the feature layer is distributed, and the features define an implicit `classification hierarchy´
fLanguage :
English
Publisher :
iet
Conference_Titel :
Artificial Neural Networks, 1989., First IEE International Conference on (Conf. Publ. No. 313)
Conference_Location :
London
Type :
conf
Filename :
51965
Link To Document :
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