The article is aimed at studying empirical estimates of the sensitivity of the second task of a posteriori inference in a knowledge pattern. The article presents the results of an experiment on finding the relationship between the distortion of incoming information and the results of learning a knowledge pattern. Formally, information distortions were achieved by changing the estimates of the fixed evidence and finding the norm of the difference between the vectors of the original and the resulting evidence. Obtaining empirical estimates is the first example of studying the second task of a posteriori inference in a knowledge pattern. The relevance of the study is emphasized by the growing popularity of machine learning and, most importantly, data preparation, since ABS is one of the few models that can work with inaccurate data.

Original languageEnglish
Title of host publicationProceedings of 2020 23rd International Conference on Soft Computing and Measurements, SCM 2020
EditorsS. Shaposhnikov
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages31-33
Number of pages3
ISBN (Electronic)9781728196923
DOIs
StatePublished - May 2020
Event23rd International Conference on Soft Computing and Measurements, SCM 2020 - St. Petersburg, Russian Federation
Duration: 27 May 202029 May 2020

Publication series

NameProceedings of 2020 23rd International Conference on Soft Computing and Measurements, SCM 2020

Conference

Conference23rd International Conference on Soft Computing and Measurements, SCM 2020
Country/TerritoryRussian Federation
CitySt. Petersburg
Period27/05/2029/05/20

    Research areas

  • algebraic Bayesian networks, knowledge pattern, logical and probabilistic graphical models, machine learning

    Scopus subject areas

  • Computational Mathematics
  • Control and Optimization
  • Computer Science Applications
  • Modelling and Simulation
  • Decision Sciences (miscellaneous)
  • Instrumentation

ID: 88231016