Machine Learning is an international forum for research on computational approaches to learning. The journal publishes articles reporting substantive results on a wide range of learning methods applied to a variety of learning problems.

The journal features papers that describe research on problems and methods, applications research, and issues of research methodology. Papers making claims about learning problems or methods provide solid support via empirical studies, theoretical analysis, or comparison to psychological phenomena. Applications papers show how to apply learning methods to solve important applications problems. Research methodology papers improve how machine learning research is conducted.

All papers describe the supporting evidence in ways that can be verified or replicated by other researchers. The papers also detail the learning component clearly and discuss assumptions regarding knowledge representation and the performance task.

  • An international forum for research on computational approaches to learning.
  • Reports substantive results on a wide range of learning methods applied to a variety of learning problems.
  • Provides solid support via empirical studies, theoretical analysis, or comparison to psychological phenomena.
  • Shows how to apply learning methods to solve important applications problems.
  • Improves how machine learning research is conducted.

Journal information

Editor-in-Chief
  • Hendrik Blockeel
Publishing model
Hybrid. Learn about publishing OA with us

Journal metrics

2.672 (2019)
Impact factor
3.157 (2019)
Five year impact factor
62 days
Submission to first decision
343 days
Submission to acceptance
776,654 (2019)
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Latest issue

Volume 109

Issue 9-10, September 2020

Special Issue of the ECML PKDD 2020 Journal Track; Guest Editors: Ira Assent, Carlotta Domeniconi, Aristides Gionis, Eyke Hüllermeier

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About this journal

Electronic ISSN
1573-0565
Print ISSN
0885-6125
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