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.
- Hendrik Blockeel
- Publishing model
- Hybrid. Learn about publishing OA with us
- 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)
Authors (first, second and last of 5)
Guest editors: Alipio Jorge, João Gama, Salvador Garcia
Submission deadline: March 1, 2021
Guest Editors: Alborz Geramifard, Lihong Li, Yuxi Li, Csaba Szepesvari, Tao Wang
Submission deadline: extended to May 15, 2020 AOE
About this journal
- Electronic ISSN
- Print ISSN
- Abstracted and indexed in
- ACM Digital Library
- Current Contents/Engineering, Computing and Technology
- EBSCO Applied Science & Technology Source
- EBSCO Associates Programs Source
- EBSCO Book Review Digest Plus
- EBSCO Computer Science Index
- EBSCO Computers & Applied Sciences Complete
- EBSCO Discovery Service
- EBSCO Engineering Source
- EBSCO Linguistics Abstracts Online
- EBSCO Military Transition Support Center
- EBSCO OmniFile
- EBSCO STM Source
- EBSCO Science Full Text Select
- EBSCO Vocational Studies
- EI Compendex
- Google Scholar
- Institute of Scientific and Technical Information of China
- Japanese Science and Technology Agency (JST)
- Journal Citation Reports/Science Edition
- Mathematical Reviews
- OCLC WorldCat Discovery Service
- ProQuest Advanced Technologies & Aerospace Database
- ProQuest Central
- ProQuest Computer Science
- ProQuest Computer and Information Systems Abstracts
- ProQuest Pharma Collection
- ProQuest SciTech Premium Collection
- ProQuest Science Database
- ProQuest Technology Collection
- ProQuest-ExLibris Primo
- ProQuest-ExLibris Summon
- Science Citation Index
- Science Citation Index Expanded (SciSearch)
- TD Net Discovery Service
- UGC-CARE List (India)
- WTI Frankfurt eG