Analysis of a Training Sample and Classification in One Recognition Modelстатья
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Дата последнего поиска статьи во внешних источниках: 8 января 2017 г.
Аннотация:A problem of classification by precedents in partial precedence models is considered. An algorithm is presented for searching for maximum logical regularities of a class (LRCs) for consistent training tables. A two-level solution scheme of a problem is proposed for finding an optimal decision rule. First, LRCs are obtained by training data, and a mapping of the initial feature descriptions of objects into a space of points of a discrete unit cube is constructed. The objects of the training sample can be divided by a hyperplane in the latter space. It is suggested that a linear decision rule in the latter space that provides the maximum gap, similar to the support vector method, should be used as the decision rule