2021年最新SCI期刊影响因子查询系统
MACHINE LEARNING 期刊详细信息
基本信息
期刊名称 | MACHINE LEARNING MACHINE LEARNING |
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期刊ISSN | 0885-6125 |
期刊官方网站 | http://link.springer.com/journal/10994 |
是否OA | 否 |
出版商 | Springer Netherlands |
出版周期 | Monthly |
始发年份 | 1986 |
年文章数 | 69 |
最新影响因子 | 5.414(2021) |
中科院SCI期刊分区
大类学科 | 小类学科 | Top | 综述 |
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工程技术3区 | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 计算机:人工智能4区 | 否 | 否 |
CiteScore
CiteScore排名 | CiteScore | SJR | SNIP | ||
---|---|---|---|---|---|
学科 | 排名 | 百分位 | 2.78 | 0.710 | 1.743 |
Computer Science Artificial Intelligence |
56 / 189 | 70% |
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Computer Science Software |
102 / 360 | 71% |
补充信息
自引率 | 0.80% |
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H-index | 124 |
SCI收录状况 |
Science Citation Index
Science Citation Index Expanded |
官方审稿时间 | Submission to first decision 61 days |
网友分享审稿时间 | 数据统计中,敬请期待。 |
PubMed Central (PML) | http://www.ncbi.nlm.nih.gov/nlmcatalog?term=0885-6125%5BISSN%5D |
投稿指南
期刊投稿网址 | https://www.editorialmanager.com/mach/ |
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收稿范围 | 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. 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. |
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