Machine Learning (journal)

{{Infobox Journal | title = Machine Learning | cover = Machine Learning (journal).jpg | discipline = [[Machine learning]] | abbreviation = Mach. Learn. | website = https://www.springer.com/west/home/computer/artificial?SGWID=4-147-70-35726603-0 | publisher = [[Kluwer]]/[[Springer Science+Business Media|Springer]] | country = [[United States|USA]] | history = 1986 to present | impact = 2.809 | impact-year = 2018 | ISSN = 1573-0565 }} '''''Machine Learning''''' is a [[peer-review]]ed [[scientific journal]], published since 1986.

In 2001, forty editors and members of the [[editorial board]] of ''Machine Learning'' resigned in order to support the ''[[Journal of Machine Learning Research]]'' (JMLR), saying that in the era of the internet, it was detrimental for researchers to continue publishing their papers in expensive journals with pay-access archives. Instead, they wrote, they supported the model of ''JMLR'', in which authors retained copyright over their papers and archives were freely available on the internet.{{cite journal | title = Editorial Board of the Kluwer Journal, Machine Learning: Resignation Letter | journal = SIGIR Forum | volume = 35 | issue = 2 | year = 2001 | url = http://sigir.org/files/forum/F2001/sigirFall01Letters.html}}

Following the mass resignation, [[Kluwer]] changed their publishing policy to allow authors to self-archive their papers online after [[Peer Review Week|peer-review]].{{cite journal|last1=Robin|first1=Peek|title=Machine Learning's Editorial Board Divided|journal=Information Today|date=1 December 2001|volume=18|issue=11|url=https://www.questia.com/magazine/1P3-95801675/machine-learning-s-editorial-board-divided|language=en|archive-date=13 August 2017|access-date=12 August 2017|archive-url=https://web.archive.org/web/20170813010220/https://www.questia.com/magazine/1P3-95801675/machine-learning-s-editorial-board-divided|url-status=dead}}

==Abstracting and indexing== The journal is abstracted and indexed in several databases, for example in:{{cite MIAR |title=Machine Learning |issn=0885-6125 |access-date=2025-06-18}} {{columns-list| *[[Science Citation Index Expanded]]{{cite web |title=Machine Learning – Abstracting and indexing |url=https://link.springer.com/journal/10994 |publisher=Springer Nature |access-date=2025-06-18}} *[[Scopus]]{{cite web |title=Machine Learning – Abstracting and indexing |url=https://link.springer.com/journal/10994 |publisher=Springer Nature |access-date=2025-06-18}} *[[EI Compendex]]{{cite web |title=Machine Learning – Abstracting and indexing |url=https://link.springer.com/journal/10994 |publisher=Springer Nature |access-date=2025-06-18}} *[[INSPEC]]{{cite web |title=Machine Learning – Abstracting and indexing |url=https://link.springer.com/journal/10994 |publisher=Springer Nature |access-date=2025-06-18}} *[[DBLP]]{{cite web |title=Machine Learning – Abstracting and indexing |url=https://link.springer.com/journal/10994 |publisher=Springer Nature |access-date=2025-06-18}} }}

== Selected articles ==

• {{cite journal | author=J.R. Quinlan | title=Induction of Decision Trees | journal=Machine Learning | volume= 1| pages=81–106 | year=1986 | doi=10.1007/BF00116251 | doi-access=free }}
• {{cite journal | author=Nick Littlestone | title=Learning Quickly When Irrelevant Attributes Abound: A New Linear-threshold Algorithm | journal=Machine Learning | volume=2 | issue=4 | pages=285–318 | year=1988 | doi=10.1007/BF00116827 | url=http://www.cs.utsa.edu/~bylander/cs6243/littlestone1988.pdf | doi-access=free | archive-date=2022-07-02 | access-date=2020-02-22 | archive-url=https://web.archive.org/web/20220702053628/http://www.cs.utsa.edu/~bylander/cs6243/littlestone1988.pdf | url-status=dead }}
• {{cite journal | author=John R. Anderson and Michael Matessa | title=Explorations of an Incremental, Bayesian Algorithm for Categorization | journal=Machine Learning | volume=9 | issue=4 | pages=275–308 | year=1992 | doi=10.1007/BF00994109 | doi-access=free }}
• {{cite journal | author=David Klahr | title=Children, Adults, and Machines as Discovery Systems | journal=Machine Learning | volume=14 | issue=3 | pages=313–320 | year=1994 | doi=10.1007/BF00993981 | doi-access=free }}
• {{cite journal | author=Thomas Dean and Dana Angluin and Kenneth Basye and Sean Engelson and Leslie Kaelbling and Evangelos Kokkevis and Oded Maron | title=Inferring Finite Automata with Stochastic Output Functions and an Application to Map Learning | journal=Machine Learning | volume=18 | pages=81–108 | year=1995 | doi=10.1007/BF00993822 | doi-access=free }}
• {{cite journal | author=Luc De Raedt and Luc Dehaspe | title=Clausal Discovery | journal=Machine Learning | volume=26 | issue=2/3 | pages=99–146 | year=1997 | doi=10.1023/A:1007361123060 | doi-access=free }}
• {{cite journal | author=C. de la Higuera | title=Characteristic Sets for Grammatical Inference | journal=Machine Learning | volume=27 | pages=1–14 | year=1997 }}
• {{cite journal | author=Robert E. Schapire and Yoram Singer | title=Improved Boosting Algorithms Using Confidence-rated Predictions | journal=Machine Learning | volume=37 | issue=3 | pages=297–336 | year=1999 | doi=10.1023/A:1007614523901 | doi-access=free }}
• {{cite journal | author=Robert E. Schapire and Yoram Singer | title=BoosTexter: A Boosting-based System for Text Categorization | journal=Machine Learning | volume=39 | issue=2/3 | pages=135–168 | year=2000 | doi=10.1023/A:1007649029923 | doi-access=free }}
• {{cite journal | author=P. Rossmanith and T. Zeugmann | title=Stochastic Finite Learning of the Pattern Languages | journal=Machine Learning | volume=44 | number=1–2 | pages=67–91 | year=2001 | doi=10.1023/A:1010875913047 | doi-access=free }}
• {{Cite journal|last1=Parekh|first1=Rajesh|last2=Honavar|first2=Vasant|date=2001|title=Learning DFA from Simple Examples|journal=Machine Learning|volume=44|issue=1/2|pages=9–35|doi=10.1023/A:1010822518073|doi-access=free}}
• {{cite journal | author=Ayhan Demiriz and Kristin P. Bennett and John Shawe-Taylor | title=Linear Programming Boosting via Column Generation | journal=Machine Learning | volume=46 | pages=225–254 | year=2002 | doi=10.1023/A:1012470815092 | doi-access=free }}
• {{cite journal | author=Simon Colton and Stephen Muggleton | title=Mathematical Applications of Inductive Logic Programming | journal=Machine Learning | volume=64 | issue=1–3 | pages=25–64 | year=2006 | doi=10.1007/s10994-006-8259-x | url=http://www.doc.ic.ac.uk/crg/papers/colton_mlj06.pdf | doi-access=free }}
• {{cite journal | author=Will Bridewell and Pat Langley and Ljupco Todorovski and Saso Dzeroski | title=Inductive Process Modeling | journal=Machine Learning | year=2008 }}
• {{cite journal | author=Stephen Muggleton and Alireza Tamaddoni-Nezhad | title=QG/GA: a stochastic search for Progol | journal=Machine Learning | volume=70 | issue=2–3 | pages=121–133 | year=2008 | doi=10.1007/s10994-007-5029-3 | doi-access=free }}

== References == {{reflist}}

[[Category:Computer science journals]] [[Category:Machine learning]] [[Category:Delayed open-access journals]] [[Category:Springer Science+Business Media academic journals]] [[Category:Academic journals established in 1986]]

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