Thematic Reviews

A section of Machine Learning and Knowledge Extraction (ISSN 2504-4990).

Section Information

Thematic Reviews provide advanced overviews, tutorials and perspective papers on the current challenges of the area of Machine Learning and Knowledge Extraction.

  • Advanced overviews aim at critically presenting the current state-of-the-art of a topic, including opposing viewpoints, and identify challenges and opportunities.
  • Tutorial papers follow a more practical approach with a focus on helping readers to quickly become familiar with particular techniques, making use of examples.
  • Perspective articles state the viewpoints of distinguished authors with respect to the current status, lessons learned and future direction of the field.

Thematic reviews will typically be by invitation only, and subjects are chosen by the Machine Learning and Knowledge Extraction (MAKE) Academic Editor. If, however, other scholars are interested in submitting a review, authors could send the tentative title and abstract to the MAKE Editorial Office ([email protected]) and the Academic Editors will pre-filter all submissions.

Keywords

machine learning; knowledge extraction; data mining; data science; big data; pattern recognition; reinforcement learning; data visualization; data ecosystems; data pre-processing; data integration; data fusion; knowledge representation; web mining; rule extraction; bioinformat-ics; interactive machine learning; human–computer interaction; data protection and security; interpretability; graph-based machine learning; probabilistic graphical models; computa-tional topology; time series

Editorial Board

Papers Published

This section is open for submissions.
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