Thematic Reviews

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 (make@mdpi.com) 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

Thematic Reviews

Section Information

Keywords

Published Papers