DocumentCode :
1818226
Title :
Cresceptron: a self-organizing neural network which grows adaptively
Author :
Weng, John ; Ahuja, Narendra ; Huang, Thomas S.
Author_Institution :
Beckman Inst., Illinois Univ., Urbana, IL, USA
Volume :
1
fYear :
1992
fDate :
7-11 Jun 1992
Firstpage :
576
Abstract :
Cresceptron uses a hierarchical framework to grow neural networks automatically, adaptively, and incrementally through learning. At every level of the hierarchy, new concepts are detected automatically and the network grows by creating new neurons and synapses which memorize the new concepts and their context. The training samples are generalized to other perceptually equivalent items through hierarchical tolerance of deviation. The neural network recognizes the learned items and their variations by hierarchically associating the learned knowledge with the input. It segments the recognized items from the input through back training along the response paths
Keywords :
hierarchical systems; learning (artificial intelligence); neural nets; self-adjusting systems; Cresceptron; hierarchical framework; neural networks; self-organizing; training samples; Backpropagation; Electric breakdown; Humans; Input variables; Learning systems; Neural networks; Neurons; Unsupervised learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1992. IJCNN., International Joint Conference on
Conference_Location :
Baltimore, MD
Print_ISBN :
0-7803-0559-0
Type :
conf
DOI :
10.1109/IJCNN.1992.287150
Filename :
287150
Link To Document :
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