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.
- Peter A. Flach
- Publishing model
- Hybrid. Open Access options available
- 2.809 (2018)
- Impact factor
- 3.203 (2018)
- Five year impact factor
- 61 days
- Submission to first decision
- 330 days
- Submission to acceptance
- 659,611 (2018)
Guest Editors: Alborz Geramifard, Lihong Li, Yuxi Li, Csaba Szepesvari, Tao Wang
Submission deadline: March 5, 2020
Guest Editors: Tim Verdonck, Bart Baesens, María Óskarsdóttir, Seppe vanden Broucke
Submission deadline: March 1st, 2020
Guest Editors: Thomas Corpetti, Dino Ienco, Roberto Interdonato, Sébastien Lefèvre, Minh-Tan Pham
Submission deadline: April 1st, 2020
Guest editors: Nikos Katzouris, Alexander Artikis, Luc de Raedt, Artur d'Avila Garcez, Ute Schmid, Sebastijan Dumančić, Jay Pujara
Cut-off dates: Feb. 15, 2020; April 15, 2020
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- Immediate online access
- Full Journal access includes all articles
- Downloadable in PDF
- Subscription expires 12/31/2020
About this journal
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- Current Contents/Engineering, Computing and Technology
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