The optimal encodings for biased association in linear associative memories
In this paper, optimal encoding schemes for linear associative memories are derived for biased association under both the white-noise and colored-noise situations. Analysis and simulation results all show that the biased encodings thus derived are optimal and superior to existing models in their performance. Together with the WeeKohonen unbiased encoding, the study settles the optimality issue of linear associative memories and enhances their practicalities.
linear associative memory biased encoding neural networks mean-square-error matrix-valued function