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Abstract <p>Lower bounds to an amount of information required to make decisions with a given admissible average error for probabilistic models of discrete source coding over distorted letters, image segmentation, and pattern recognition (object classification) have been studied. The indicated bounds have been constructed as generalizations of the well-known information-theoretic trade-off relation in the form of a rate distortion function with Hamming distortion measure. The proposed bounds are independent on decision algorithms and are useful for evaluating an efficiency of the algorithms in terms of a redundancy of the amount of information used by an algorithm with an acceptable error probability or a redundancy of the algorithm error probability relative to the boundary value at the specified amount of information used.</p>

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Boundary Relations between Amount of Information and Decision Fidelity for Data Coding and Analysis Models

  • 期刊论文

  • 发表日期：

    2025年9月1日

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  • M. M. Lange

摘要

Abstract

Lower bounds to an amount of information required to make decisions with a given admissible average error for probabilistic models of discrete source coding over distorted letters, image segmentation, and pattern recognition (object classification) have been studied. The indicated bounds have been constructed as generalizations of the well-known information-theoretic trade-off relation in the form of a rate distortion function with Hamming distortion measure. The proposed bounds are independent on decision algorithms and are useful for evaluating an efficiency of the algorithms in terms of a redundancy of the amount of information used by an algorithm with an acceptable error probability or a redundancy of the algorithm error probability relative to the boundary value at the specified amount of information used.