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.
Editor-in-chief
  • Peter A. Flach
Impact
Impact factor: 2.809 (2018)
Five year impact factor: 3.203 (2018)
Speed
Submission to first decision: 63 days
Acceptance to publication: 18 days
Usage
Downloads: 659,611 (2018)
Publishing model
Hybrid. Open Choice – What is this?

Articles

Subscribe

Subscription
$199.00
Note this is only the net price. Taxes will be calculated during checkout.
  • Immediate online access
  • Full Journal access includes all articles
  • Downloadable in PDF
  • Subscription expires 12/31/2019

Advertisement

About this journal

Electronic ISSN
1573-0565
Print ISSN
0885-6125
Abstracted and indexed in
  1. ACM Digital Library
  2. Current Contents/Engineering, Computing and Technology
  3. DBLP
  4. EBSCO Discovery Service
  5. EI Compendex
  6. Gale
  7. Gale Academic OneFile
  8. Gale InfoTrac
  9. Google Scholar
  10. INSPEC
  11. Institute of Scientific and Technical Information of China
  12. Japanese Science and Technology Agency (JST)
  13. Journal Citation Reports/Science Edition
  14. Mathematical Reviews
  15. Naver
  16. OCLC WorldCat Discovery Service
  17. ProQuest Advanced Technologies & Aerospace Database
  18. ProQuest Central
  19. ProQuest Computer Science
  20. ProQuest Computer and Information Systems Abstracts
  21. ProQuest Pharma Collection
  22. ProQuest SciTech Premium Collection
  23. ProQuest Science Database
  24. ProQuest Technology Collection
  25. ProQuest-ExLibris Primo
  26. ProQuest-ExLibris Summon
  27. SCImago
  28. SCOPUS
  29. Science Citation Index
  30. Science Citation Index Expanded (SciSearch)
  31. WTI Frankfurt eG
  32. zbMATH
Copyright information

Rights and permissions

Springer Nature policies

© The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature