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期刊名
MACHINE LEARNING
MACH LEARN
ISSN / eISSN
0885-6125 / 1573-0565
目标和范围
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.
研究方向
计算机：人工智能
CiteScore
11.00
查看趋势图
CiteScore 学科排名
| 类别 | 分区 | 排名 |
|---|---|---|
| Computer Science - Software | Q1 | #45/407 |
| Computer Science - Artificial Intelligence | Q1 | #54/350 |
Web of Science 核心收藏夹
| Science Citation Index Expanded (SCIE) | Social Sciences Citation Index (SSCI) |
|---|---|
| Indexed | - |
| 类别 (Journal Citation Reports 2024) | 分区 |
|---|---|
| COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Q2 |
H-index
135
出版国家或地区
UNITED STATES
出版商
Springer US
出版周期
Monthly
出版年份
1986
年文章数
164
Open Access
NO
通讯方式
SPRINGER, VAN GODEWIJCKSTRAAT 30, DORDRECHT, NETHERLANDS, 3311 GZ
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