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Algebraic Bayesian networks : A frequentist approach to knowledge pattern parameters machine learning. / Tulupyev, Alexandr ; Kharitonov, Nikita.

в: CEUR Workshop Proceedings, Том 2782, 2020, стр. 65-70.

Результаты исследований: Научные публикации в периодических изданиях › статья в журнале по материалам конференции › Рецензирование

BibTeX

@article{2eb2f1637ba24c9e9dcf258d2cc64e92,

title = "Algebraic Bayesian networks: A frequentist approach to knowledge pattern parameters machine learning",

abstract = "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{\textquoteright}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.",

keywords = "Algebraic Bayesian networks, Frequentist approach, Machine learning, Probabilistic graphical model",

author = "Alexandr Tulupyev and Nikita Kharitonov",

note = "Publisher Copyright: {\textcopyright} 2020 CEUR-WS. All rights reserved.; Russian 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 ; Conference date: 29-06-2020 Through 01-07-2020",

year = "2020",

language = "English",

volume = "2782",

pages = "65--70",

journal = "CEUR Workshop Proceedings",

issn = "1613-0073",

publisher = "RWTH Aahen University",

}

RIS

TY - JOUR

T1 - Algebraic Bayesian networks

T2 - Russian 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

AU - Tulupyev, Alexandr

AU - Kharitonov, Nikita

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - Algebraic Bayesian networks

KW - Frequentist approach

KW - Machine learning

KW - Probabilistic graphical model

UR - http://www.scopus.com/inward/record.url?scp=85099031210&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:85099031210

VL - 2782

SP - 65

EP - 70

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

SN - 1613-0073

Y2 - 29 June 2020 through 1 July 2020

ER -

ID: 88231184

Algebraic Bayesian networks: A frequentist approach to knowledge pattern parameters machine learning

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