MACHINE LEARNING
Note: The following journal information is for reference only. Please check the journal website for updated information prior to submission.
Journal Title
MACHINE LEARNING
MACH LEARN
ISSN / eISSN
0885-6125 / 1573-0565
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.
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.
Subject Area
COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
CiteScore
11.00
View Trend
CiteScore Ranking
| Category | Quartile | Rank |
|---|---|---|
| Computer Science - Software | Q1 | #45/407 |
| Computer Science - Artificial Intelligence | Q1 | #54/350 |
Web of Science Core Collection
| Science Citation Index Expanded (SCIE) | Social Sciences Citation Index (SSCI) |
|---|---|
| Indexed | - |
| Category (Journal Citation Reports 2024) | Quartile |
|---|---|
| COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Q2 |
H-index
135
Country/Area of Publication
UNITED STATES
Publisher
Springer US
Publication Frequency
Monthly
Year Publication Started
1986
Annual Article Volume
164
Open Access
NO
Contact
SPRINGER, VAN GODEWIJCKSTRAAT 30, DORDRECHT, NETHERLANDS, 3311 GZ
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