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Machine learning on small size samples: A synthetic knowledge synthesis.

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This summary is machine-generated.

The small data problem in machine learning is a significant challenge, despite big data prevalence. This study synthesizes research on solutions for machine learning with limited datasets.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Data Science

Background:

  • Machine learning (ML) is crucial for managing digital world complexity.
  • The 'big data' era presents challenges with small data samples in real-world research.
  • Addressing the small data problem is vital for advancing ML applications.

Purpose of the Study:

  • To investigate the small data problem in machine learning.
  • To identify and synthesize existing solutions for machine learning with small datasets.
  • To analyze the research landscape and trends in this domain.

Main Methods:

  • Bibliometric knowledge synthesis.
  • Trend analysis of research publications and community growth.
  • Thematic analysis of research literature.

Main Results:

  • A growing research community and publication trend indicate field maturity.
  • China, the United States, and the United Kingdom are leading research producers.
  • Research themes include dimension reduction, data augmentation, data mining, and statistical learning on small datasets.

Conclusions:

  • The small data problem is an active and maturing research area within machine learning.
  • International collaboration exists, but research is concentrated in developed countries.
  • Key solutions involve techniques applicable to dimension reduction, data augmentation, and statistical learning for limited data scenarios.