The ART of adaptive pattern recognition by a self-organizing neuralnetwork
Carpenter, G.A.; Grossberg, S.
Computer
Volume 21, Issue 3, Mar 1988 Page(s):77 - 88
Digital Object Identifier 10.1109/2.33
Summary:The adaptive resonance theory (ART) suggests a solution to the
stability-plasticity dilemma facing designers of learning systems,
namely how to design a learning system that will remain plastic, or
adaptive, in response to significant events and yet remain stable in
response to irrelevant events. ART architectures are discussed that are
neural networks that self-organize stable recognition codes in real time
in response to arbitrary sequences of input patterns. Within such an ART
architecture, the process of adaptive pattern recognition is a special
case of the more general cognitive process of hypothesis discovery,
testing, search, classification, and learning. This property opens up
the possibility of applying ART systems to more general problems of
adaptively processing large abstract information sources and databases.
The main computational properties of these ART architectures are
outlined and contrasted with those of alternative learning and
recognition systems
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