Information-Theoretic Lower Bounds to Error Probability for the Models of Noisy Discrete Source Coding and Object Classification

Probabilistic models of noisy discrete source coding and object classification are studied. For these models, the appropriate minimal information amounts as the functions of a given admissible error probability are defined and the strictly decreasing lower bounds to these functions are constructed....

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