Abstract
The discriminating capabilities of a random subspace classifier are considered. As a result of analysis of the probability density distribution of threshold values, an estimate is obtained for the minimum distinguishable distance. Real examples of separating surfaces for classical two-imensional problems are given. An algorithm is proposed for local averaging of a synapse matrix to improve the classifier performance in solving problems with overlapping probability distributions. The random subspace classifier is proved to be universal.
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