Machine Learning

杂志介绍

Machine Learning杂志介绍

《Machine Learning》是一本以English为主的未开放获取国际优秀期刊，中文名称机器学习，本刊主要出版、报道计算机科学-COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE领域的研究动态以及在该领域取得的各方面的经验和科研成果，介绍该领域有关本专业的最新进展，探讨行业发展的思路和方法，以促进学术信息交流，提高行业发展。该刊已被国际权威数据库SCIE收录，为该领域相关学科的发展起到了良好的推动作用，也得到了本专业人员的广泛认可。该刊最新影响因子为4.3，最新CiteScore 指数为11。

本刊近期中国学者发表的论文主要有：

• Multiscale principle of relevant information for hyperspectral image classification

  Author: Wei, Yantao; Yu, Shujian; Giraldo, Luis Sanchez; Principe, Jose C.

• Troubleshooting image segmentation models with human-in-the-loop

  Author: Wang, Haotao; Chen, Tianlong; Wang, Zhangyang; Ma, Kede

• Beyond confusion matrix: learning from multiple annotators with awareness of instance features

  Author: Li, Jingzheng; Sun, Hailong; Li, Jiyi

• DAFS: a domain aware few shot generative model for event detection

  Author: Xia, Nan; Yu, Hang; Wang, Yin; Xuan, Junyu; Luo, Xiangfeng

英文介绍

Machine Learning杂志英文介绍

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.