Who publishes Machine Learning?

Machine Learning is published by Springer.

What is the ISSN of Machine Learning?

The ISSN of Machine Learning is 0885-6125.

What is the impact factor of Machine Learning?

The reported impact factor of Machine Learning is 2.9. Impact factor values are reproduced from publishers and may lag the latest JCR release.

What is the CCF ranking of Machine Learning?

Machine Learning is ranked CCF B in the China Computer Federation journal catalogue.

ジャーナル情報

Machine Learning

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インパクトファクター:

2.9

出版社:

Springer

ISSN:

0885-6125

閲覧:

28861

フォロー:

46

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論文募集

Machine Learning is an academic journal published by Springer. (ISSN 0885-6125, impact factor 2.9, CCF B).

Aims and scope 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, including but not limited to: Learning Problems: Classification, regression, recognition, and prediction; Problem solving and planning; Reasoning and inference; Data mining; Web mining; Scientific discovery; Information retrieval; Natural language processing; Design and diagnosis; Vision and speech perception; Robotics and control; Combinatorial optimization; Game playing; Industrial, financial, and scientific applications of all kinds. Learning Methods: Supervised and unsupervised learning methods (including learning decision and regression trees, rules, connectionist networks, probabilistic networks and other statistical models, inductive logic programming, case-based methods, ensemble methods, clustering, etc.); Reinforcement learning; Evolution-based methods; Explanation-based learning; Analogical learning methods; Automated knowledge acquisition; Learning from instruction; Visualization of patterns in data; Learning in integrated architectures; Multistrategy learning; Multi-agent learning. Papers describe research on problems and methods, applications research, and issues of research methodology. Papers making claims about learning problems (e.g., inherent complexity) or methods (e.g., relative performance of alternative algorithms) 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 must state their contributions clearly and describe how the contributions are supported. All papers must describe the supporting evidence in ways that can be verified or replicated by other researchers. All papers must describe the learning component clearly, and must discuss assumptions regarding knowledge representation and the performance task. All papers must place their contribution clearly in the context of existing work in machine learning. Variations from these prototypes, such as comprehensive surveys of active research areas, critical reviews of existing work, and book reviews, will be considered provided they make a clear contribution to the field.

最終更新：Dou Sun（2025-12-29）

CCF	正式名称	インパクトファクター	出版社	ISSN
C	Machine Vision and Applications	2.3	Springer	0932-8092
C	Machine Translation		Springer	0922-6567
	Computing	2.8	Springer	0010-485X
	Smart Learning Environments	12.1	Springer	2196-7091
	Networking Science		Springer	2076-0310
	Information Technology and Management	2.9	Springer	1385-951X
C	Applied Intelligence	3.5	Springer	0924-669X
	Security Informatics		Springer	2190-8532
	International Journal of Information Security	3.2	Springer	1615-5262
C	Wireless Networks	2.1	Springer	1022-0038

CCF	ICORE	CP-I	略称	正式名称	投稿締切	開催日
		54.1	L&T	International Learning and Technology Conference	2026-10-31	2027-02-01
		52.8	MLMI	International Conference on Machine Learning and Machine Intelligence	2027-02-05	2027-07-16
		39.5	AIMLMI	Asia Conference on Advanced Artificial Intelligence, Machine Learning and Man-machine Interaction	2023-07-30	2023-08-18
A	A*	91.9	ICML	International Conference on Machine Learning	2026-01-23	2026-07-06
		59.4	ICMLC	International Conference on Machine Learning and Computing	2026-09-25	2027-02-26
		42.3	DSML'	International Conference on Data Science and Machine Learning	2023-10-14	2023-10-28
		43.6	ICML'	International Conference on Intelligent Communication and Machine Learning	2024-09-30	2024-11-22
		46.2	BDML'	International Conference on Big Data and Machine Learning	2026-06-13	2026-06-27
		47.6	BDML	International Conference on Big Data and Machine Learning	2025-11-15	2025-12-12
		42.7	Deep-ML	International Conference on Deep Learning and Machine Learning in Emerging Applications	2019-04-23	2019-08-26

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