Machine Learning
Overview
Machine Learning is a renowned interdisciplinary journal that focuses on the latest advancements in the field of artificial intelligence and data analysis. It publishes cutting-edge research articles, reviews, and case studies that explore the theory, algorithms, and applications of machine learning techniques. With a broad scope and rigorous peer-review process, this journal serves as a valuable resource for researchers, practitioners, and students interested in the rapidly evolving field of machine learning.
Details
Details
Abbr.
Mach Learn
Start
1986
End
Continuing
Frequency
8 no. a year, <1992->
p-ISSN
0885-6125
e-ISSN
1573-0565
Country
United States
Language
English
Metrics
Metrics
h-index / Ranks: 1102
169
SJR / Ranks: 1540
1720
CiteScore / Ranks: 2014
8.50
JIF / Ranks: 763
7.5
Recent Articles
1.
Liapis G, Tsoka S, Papageorgiou L
Mach Learn
. 2025 Feb;
114(3):51.
PMID: 40017483
Data classification is considered a fundamental research subject within the machine learning community. Researchers seek the improvement of machine learning algorithms in not only accuracy, but also interpretability. Interpretable algorithms...
2.
Lock E
Mach Learn
. 2025 Jan;
113(10):7451-7477.
PMID: 39759800
Data for several applications in diverse fields can be represented as multiple matrices that are linked across rows or columns. This is particularly common in molecular biomedical research, in which...
3.
Ghosh S, Kim R, Chhabria P, Dwivedi R, Klasnja P, Liao P, et al.
Mach Learn
. 2024 Sep;
113(7):3961-3997.
PMID: 39221170
There is a growing interest in using reinforcement learning (RL) to personalize sequences of treatments in digital health to support users in adopting healthier behaviors. Such sequential decision-making problems involve...
4.
Clark B, Wilming R, Haufe S
Mach Learn
. 2024 Aug;
113(9):6871-6910.
PMID: 39132312
The field of 'explainable' artificial intelligence (XAI) has produced highly acclaimed methods that seek to make the decisions of complex machine learning (ML) methods 'understandable' to humans, for example by...
5.
Yang J, El-Bouri R, ODonoghue O, Lachapelle A, Soltan A, Eyre D, et al.
Mach Learn
. 2024 May;
113(5):2655-2674.
PMID: 38708086
Supplementary Information: The online version contains supplementary material available at 10.1007/s10994-023-06481-z.
6.
Karagiannaki I, Gourlia K, Lagani V, Pantazis Y, Tsamardinos I
Mach Learn
. 2023 Oct;
112(11):4257-4287.
PMID: 37900054
Molecular gene-expression datasets consist of samples with tens of thousands of measured quantities (i.e., high dimensional data). However, lower-dimensional representations that retain the useful biological information do exist. We present...
7.
Berkenkamp F, Krause A, Schoellig A
Mach Learn
. 2023 Sep;
112(10):3713-3747.
PMID: 37692295
Selecting the right tuning parameters for algorithms is a pravelent problem in machine learning that can significantly affect the performance of algorithms. Data-efficient optimization algorithms, such as Bayesian optimization, have...
8.
Schnorr D, Schnorr C
Mach Learn
. 2023 Aug;
112(9):3151-3190.
PMID: 37575882
The Turing mechanism describes the emergence of spatial patterns due to spontaneous symmetry breaking in reaction-diffusion processes and underlies many developmental processes. Identifying Turing mechanisms in biological systems defines a...
9.
Goethals S, Martens D, Calders T
Mach Learn
. 2023 Jun;
:1-32.
PMID: 37363047
This paper studies how counterfactual explanations can be used to assess the fairness of a model. Using machine learning for high-stakes decisions is a threat to fairness as these models...
10.
Schurch M, Azzimonti D, Benavoli A, Zaffalon M
Mach Learn
. 2023 May;
112(5):1411-1432.
PMID: 37162796
Supplementary Information: The online version contains supplementary material available at 10.1007/s10994-022-06297-3.