A Neuronet Algorithm for Recognizing a Large Number of Highly Correlated Patterns
نویسندگان
چکیده
The paper gives a simple algorithm that allows us to eliminate correlation between input binary patterns by changing their dimensionality. A neural network that is a variant of vector associative memory is used to recognize redimensioned patterns. Having capacity much greater than conventional neural networks, the resulting associative memory can recognize highly noisy and correlated input patterns.
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