
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 (Transformative Journal). Learn about publishing Open Access with us
Journal metrics
- 2.672 (2019)
- Impact factor
- 3.157 (2019)
- Five year impact factor
- 79 days
- Submission to first decision
- 319 days
- Submission to acceptance
- 956,524 (2020)
- Downloads
Latest issue

Latest articles
-
-
-
SPEED: secure, PrivatE, and efficient deep learning
Authors (first, second and last of 5)
This is part of 1 collection: -
Bayesian optimization with approximate set kernels
Authors (first, second and last of 5)
This is part of 1 collection: -
Journal updates
-
Call for Papers: Special Issue on Foundations of Data Science
Guest editors: Alipio Jorge, João Gama, Salvador Garcia
Submission deadline: March 1, 2021 -
Call for Papers: Special Issue on Discovery Science 2020
Guest editors: Annalisa Appice, Grigorious Tsoumakas
Submission deadline: extended to March 1, 2021 -
Call for Papers: Special Issue on Safe and Fair Machine Learning
Guest editors: Dana Drachsler Cohen, Javier Garcia, Mohammad Ghavamzadeh, Marek Petrik, Philip S. Thomas
Submission deadline: 15 November 2021
About this journal
- Electronic ISSN
- 1573-0565
- Print ISSN
- 0885-6125
- Abstracted and indexed in
-
- ACM Digital Library
- ANVUR
- CNKI
- Current Contents/Engineering, Computing and Technology
- DBLP
- Dimensions
- 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
- INSPEC
- Institute of Scientific and Technical Information of China
- Japanese Science and Technology Agency (JST)
- Journal Citation Reports/Science Edition
- Mathematical Reviews
- Naver
- 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
- SCImago
- SCOPUS
- Science Citation Index
- Science Citation Index Expanded (SciSearch)
- TD Net Discovery Service
- UGC-CARE List (India)
- WTI Frankfurt eG
- zbMATH
- Copyright information
-
© The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature