Abstract
As applied to the description of a Fourier-holography layout with phase
conjugation in the correlation plane as the implementation of a two-layer neural
network, this paper discusses two models of the advancement of hypotheses: linear
regression of the conditions of a problem from knowledge, and inductive inference. The
factors that influence the adequacy of the hypotheses generated for the conditions of a
problem are determined and numerically investigated. It is shown that the adequacy of
the hypotheses increases as the number of spatial degrees of freedom of the patterns
that represent the conditions of the problem (the generalized frequency) increases;
moreover, because of internal correlation (as an attribute of the information),
increasing the size of the pattern influences the adequacy more effectively than does
high-frequency filtering.
© 2013 Optical Society of America
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