An Extension of the Traditional Classi cation Rules: the Case of Non-Random Samples
The 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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