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Machine Learning (journal)

Machine Learning is a peer-reviewed scientific journal, published since 1986.

Discipline

Machine learning

English

1986 to present

2.809 (2018)

Mach. Learn.

1573-0565

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.[1]


Following the mass resignation, Kluwer changed their publishing policy to allow authors to self-archive their papers online after peer-review.[2]

J.R. Quinlan (1986). . Machine Learning. 1: 81–106. doi:10.1007/BF00116251.

"Induction of Decision Trees"

Nick Littlestone (1988). (PDF). Machine Learning. 2 (4): 285–318. doi:10.1007/BF00116827.

"Learning Quickly When Irrelevant Attributes Abound: A New Linear-threshold Algorithm"

John R. Anderson and Michael Matessa (1992). . Machine Learning. 9 (4): 275–308. doi:10.1007/BF00994109.

"Explorations of an Incremental, Bayesian Algorithm for Categorization"

David Klahr (1994). . Machine Learning. 14 (3): 313–320. doi:10.1007/BF00993981.

"Children, Adults, and Machines as Discovery Systems"

Thomas Dean and Dana Angluin and Kenneth Basye and Sean Engelson and Leslie Kaelbling and Evangelos Kokkevis and Oded Maron (1995). . Machine Learning. 18: 81–108. doi:10.1007/BF00993822.

"Inferring Finite Automata with Stochastic Output Functions and an Application to Map Learning"

Luc De Raedt and Luc Dehaspe (1997). . Machine Learning. 26 (2/3): 99–146. doi:10.1023/A:1007361123060.

"Clausal Discovery"

C. de la Higuera (1997). "Characteristic Sets for Grammatical Inference". Machine Learning. 27: 1–14.

Robert E. Schapire and Yoram Singer (1999). . Machine Learning. 37 (3): 297–336. doi:10.1023/A:1007614523901.

"Improved Boosting Algorithms Using Confidence-rated Predictions"

Robert E. Schapire and Yoram Singer (2000). . Machine Learning. 39 (2/3): 135–168. doi:10.1023/A:1007649029923.

"BoosTexter: A Boosting-based System for Text Categorization"

P. Rossmanith and T. Zeugmann (2001). . Machine Learning. 44 (1–2): 67–91. doi:10.1023/A:1010875913047.

"Stochastic Finite Learning of the Pattern Languages"

Parekh, Rajesh; Honavar, Vasant (2001). . Machine Learning. 44 (1/2): 9–35. doi:10.1023/A:1010822518073.

"Learning DFA from Simple Examples"

Ayhan Demiriz and Kristin P. Bennett and John Shawe-Taylor (2002). . Machine Learning. 46: 225–254. doi:10.1023/A:1012470815092.

"Linear Programming Boosting via Column Generation"

Simon Colton and Stephen Muggleton (2006). (PDF). Machine Learning. 64 (1–3): 25–64. doi:10.1007/s10994-006-8259-x.

"Mathematical Applications of Inductive Logic Programming"

Will Bridewell and Pat Langley and Ljupco Todorovski and Saso Dzeroski (2008). "Inductive Process Modeling". Machine Learning.

Stephen Muggleton and Alireza Tamaddoni-Nezhad (2008). . Machine Learning. 70 (2–3): 121–133. doi:10.1007/s10994-007-5029-3.

"QG/GA: a stochastic search for Progol"