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