An Extension of the Traditional Classi cation Rules: the Case of Non-Random Samples
AbstractThe paper deals with an heuristic generalization of the traditional classi cation rules by incorporating within sample dependencies. The main motivation behind this generalization is to develop a new classi cation rule when training samples are not random, but, jointly equicorrelated.
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Bibliographic InfoPaper provided by College of Business, University of Texas at San Antonio in its series Working Papers with number 0057.
Length: 14 pages
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Classi cation rules; Non-random samples; Jointly equicorrelated training vectors;
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