Algebraic Bayesian networks are related to the class of probabilistic graphical models. As a machine learning model they are required to be trained on some data set. This work is dedicated to the frequentist approach to machine learning of a knowledge pattern as a local learning of the Algebraic Bayesian network. The theoretical explanation of approach is provided and the algorithm is described. The algorithm’s pseudocode is presented, its theoretical complexity is calculated. Then an experiment is conducted and real estimates of the algorithm's implementation time of work are received.

Original languageEnglish
Pages (from-to)65-70
Number of pages6
JournalCEUR Workshop Proceedings
Volume2782
StatePublished - 2020
EventRussian Advances in Fuzzy Systems and Soft Computing: Selected Contributions to the 8th International Conference on "Fuzzy Systems, Soft Soft Computing and Intelligent Technologies",FSSCIT 2020 - Smolensk, Russian Federation
Duration: 29 Jun 20201 Jul 2020

    Scopus subject areas

  • Computer Science(all)

    Research areas

  • Algebraic Bayesian networks, Frequentist approach, Machine learning, Probabilistic graphical model

ID: 88231184