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Metodoloģijas izstrāde zināšanu izgūšanai no apmācītiem mākslīgajiem neironu tīkliem

Summary of the Doctoral Thesis

Andrejs Bondarenko, Riga Technical University, Latvia
ORCID iD http://orcid.org/0000-0002-4103-6814

Artificial neural networks (ANN) are widely used in machine learning. They are powerful non-linear models that can be trained in a supervised, semi-supervised, and unsupervised manner. There is no single best machine learning classifier that can be used in all scenarios, but ANNs are frequently outperforming other classifiers. On the downside, it is hard to explain how classification decision is made within ANN. Artificial neural networks are essentially black-boxes. Lack of understanding of how such classifiers work severely limits their applicability. The Thesis is devoted to the development of approaches allowing to extract knowledge in the form of rules from trained ANN classifier.

Additional information

Publication type

DOI

https://doi.org/10.7250/9789934109409

Hyperlink

https://ortus.rtu.lv/science/en/publications/30953

Defence date

18.05.2020.

ISBN (print)

978-9934-22-006-7

ISBN (pdf)

978-9934-10-940-9

Format

Pages

45

Published online

Publisher

RTU Press

Country of Publication

Latvia

Publication language

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