Machine Learning

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.

《Machine Learning》是一本由Springer US出版商出版的专业计算机科学期刊，该刊创刊于1986年，刊期Monthly，该刊已被国际权威数据库SCIE收录。在中科院最新升级版分区表中，该刊分区信息为大类学科：计算机科学3区，小类学科：计算机：人工智能 3区；在JCR（Journal Citation Reports）分区等级为Q2。该刊发文范围涵盖COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE等领域，旨在及时、准确、全面地报道国内外COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE工作者在该领域取得的最新研究成果、工作进展及学术动态、技术革新等，促进学术交流，鼓励学术创新。2023年影响因子为4.3，平均审稿速度 较慢,6-12周 。