The results obtained in [1, 2] are developed in this paper. The problem of estimating the amount of learning information actually used for recognizing a fixed point in local recurrent stochastic recognition algorithms is considered. It is demonstrated that, under certain conditions, O(lnn) represents the upper estimate for the amount mentioned above, where n is the size of the entire learning sample.
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