Abstract—

This paper discusses approaches to evaluating the quality of intelligent data analysis results in diagnostic tasks. The reliability (indisputability) of empirical dependencies established during training (interpolation–extrapolation) on precedents is evaluated using a special mathematical tool, that is, characteristic functions. Characteristic functions are generated on the available sample of empirical data based on similarity analysis of precedent descriptions, formalized as a binary algebraic operation. Some estimates of the computational complexity of applying the proposed mathematical technique of characteristic functions to predicting (diagnosing) the properties of newly studied precedents are presented.

中文翻译：

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使用特征函数预测新对象的属性时计算复杂性的一些估计

摘要-

本文讨论了评估诊断任务中智能数据分析结果质量的方法。使用特殊的数学工具（即特征函数）评估对先例进行训练（内插-外推）时建立的经验依存关系的可靠性（不可争议）。基于对先验描述的相似性分析，在经验数据的可用样本上生成特征函数，形式化为二进制代数运算。提出了一些计算复杂性的估计，这些计算复杂性是将拟议的特征函数数学技术应用到预测（诊断）新近研究的先例的性质上的。