A neural model for category learning
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Douglas L. Reilly; Leon N. Cooper; Charles Elbaum
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Article
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1982
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Springer-Verlag
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English
โ 684 KB
We present a general neural model for supervised learning of pattern categories which can resolve pattern classes separated by nonlinear, essentially arbitrary boundaries. The concept of a pattern class develops from storing in memory a limited number of class elements (prototypes). Associated with