Some Estimates of Computational Complexity When Predicting the Properties of New Objects Using Characteristic Functions

• Published in: Automatic documentation and mathematical linguistics Vol. 54; no. 6; pp. 298 - 305
• Main Author: Zabezhailo, M. I.
• Format: Journal Article
• Language: English
• Published: Moscow Pleiades Publishing 01.11.2020; Springer Nature B.V
• Subjects: Characteristic functions; Complexity; Computer Science; Data analysis; Empirical analysis; Information Analysis; Information Storage and Retrieval; Interpolation; Mathematical analysis; Reliability analysis; intelligent data analysis; causal analysis; empirical dependencies; diagnostic problems; computational complexity
• ISSN: 0005-1055; 1934-8371
• DOI: 10.3103/S0005105520060072

— 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.