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- W2971199310 abstract "Information technology allowing one to implement the tasks of classification, clustering, studying the topology of the data of the information component is proposed. The multidimensional feature space is reduced to the visual presentation space to determine the information content of the data. Optimized reduction of the space dimension to two-dimensional one applying multidimensional scaling methods is used. Visual definition of grouping data allows separating areas to form. The next stage is visual limitation of categories of classes using graphic separators. To enable flexibility of nonlinear areas limitation a combination of linear ones is used, thereby forming a piecewise linear set with necessary degree of sampling. Using piecewise linear constraints allows us to implement projecting into original multidimensional feature space. Visual construction of restrictive separators makes it possible to consider tolerance fields of changing of features parameters, separation measure of classes, nonlinearity of data grouping. This is followed by reverse expansion of space with the projection of the separators into n -dimensional space with the separating hyperspace synthesis. Thus the limitative areas of hyperspace for the necessary categories of classes are formed. At the same time the visualization of classification process in a hyperspace is provided. The information technology base is the multidimensional space projection into visual (two-dimensional) space construction piecewise linear limiters of studied areas, subsequent limiters projecting into multidimensional space. Thus the information technology enables us to synthesize separating hyperplanes limiting categories of classes in multidimensional space. The technology application successive stages are described." @default.
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- W2971199310 date "2019-01-01" @default.
- W2971199310 modified "2023-09-27" @default.
- W2971199310 title "Information Technology of Separating Hyperplanes Synthesis for Linear Classifiers" @default.
- W2971199310 doi "https://doi.org/10.1615/jautomatinfscien.v51.i5.50" @default.
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