A real-time model of parallel distributed pattern learning that permits fast as well as slow adaptation, without catastrophic forgetting.
Abstract |
ART and ARTMAP neural networks for adaptive recognition and prediction have been applied to a variety of problems. Applications include parts design retrieval at the Boeing Company, automatic mapping from remote sensing ... |
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Abstract |
A class of adaptive resonance theory (ART) models for learning, recognition, and prediction with arbitrarily distributed code representations is introduced. Distributed ART neural networks combine the stable fast learning ... |
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Abstract |
A class of adaptive resonance theory (ART) models for learning, recognition, and prediction with arbitrarily distributed code representations is introduced. Distributed ART neural networks combine the stable fast learning ... |
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Abstract |
How do humans and animals learn to recognize objects and events? Two classical views are that exemplars or prototypes are learned. A hybrid view is that a mixture, called rule-plus-exceptions, is learned. None of these ... |
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Abstract |
Distributed coding at the hidden layer of a multi-layer perceptron (MLP) endows the network with memory compression and noise tolerance capabilities. However, an MLP typically requires slow off-line learning to avoid ... |
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Abstract |
ART and ARTMAP neural networks for adaptive recognition and prediction have been applied to a variety of problems, including automatic mapping from remote sensing satellite measurements, parts design retrieval at the Boeing ... |
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Abstract |
Distributed coding at the hidden layer of a multi?layer perceptron (MLP) endows the network with memory compression and noise tolerance capabilities. However, an MLP typically requires slow off?line learning to avoid ... |
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Abstract |
In order to benefit from the advantages of localist coding neural models that feature winner-take-all representations at the top level of a network hierarchy must still solve the computational problems inherent in ... |
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Abstract |
Adaptive resonance is a theory of cognitive information processing which has been realized as a family of neural network models. In recent years, these models have evolved to incorporate new capabilities in the cognitive, ... |
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Abstract |
ART (Adaptive Resonance Theory) neural networks for fast, stable learning and prediction have been applied in a variety of areas. Applications include airplane design and manufacturing, automatic target recognition, ... |
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Abstract |
Real-time neural-network models provide a conceptual framework for formulating questions about the nature of cognition, an architectural framework for mapping cognitive functions to brain regions, a semantic framework for ... |
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Description |
The Self-Organizing ARTMAP Rule Discovery (SOARD) system derives relationships among recognition classes during online learning. SOARD training on input/output pairs produces direct recognition of individual class labels for ... |
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