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- W1965766105 abstract "In this paper we introduce two pattern classifiers for non-sparse data (i.e. data with overlapping class distributions) which use the optimal interpolative neural network (OI-net), derived by one of the authors based on a generalized Fock (GF) space formulation. We present a statistical pattern classifier operating as a two-stage algorithm. The first stage consists of a pre-processing operation involving a k-N N editing of the original training set T. The operation results in a new training set, Te, which in the second stage is classified by an OI-net constructed by the recursive least squares algorithm. We also propose a new data specific classifier which has an additional third computational stage, in which samples of the original training set are added to the network piece by piece until satisfactory classification results are obtained. During the computation process the training set is iteratively updated until the number of mis-classified samples is minimized. The performance of these two classifiers has been evaluated in some illustrative examples." @default.
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- W1965766105 date "1996-04-01" @default.
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- W1965766105 title "Pattern classification of non-sparse data using optimal interpolative nets" @default.
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- W1965766105 doi "https://doi.org/10.1016/0925-2312(95)00045-3" @default.
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