Machine learning on small size samples: A synthetic knowledge synthesis

One of the increasingly important technologies dealing with the growing complexity of the digitalization of almost all human activities is Artificial intelligence, more precisely machine learning Despite the fact, that we live in a Big data world where almost everything is digitally stored, there ar...

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Bibliographic Details
Published inarXiv.org
Main Authors Kokol, Peter, Kokol, Marko, Zagoranski, Sašo
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 01.03.2021
Subjects
Artificial intelligence
Bibliometrics
Datasets
Digitization
International cooperation
Machine learning
Regional development
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Summary:One of the increasingly important technologies dealing with the growing complexity of the digitalization of almost all human activities is Artificial intelligence, more precisely machine learning Despite the fact, that we live in a Big data world where almost everything is digitally stored, there are many real-world situations, where researchers are faced with small data samples. The present study aim is to answer the following research question namely What is the small data problem in machine learning and how it is solved?. Our bibliometric study showed a positive trend in the number of research publications concerning the use of small datasets and substantial growth of the research community dealing with the small dataset problem, indicating that the research field is moving toward higher maturity levels. Despite notable international cooperation, the regional concentration of research literature production in economically more developed countries was observed.
ISSN:2331-8422