Machine Learning

雜志介紹

Machine Learning雜志介紹

《Machine Learning》是一本以English為主的未開放獲取國際優(yōu)秀期刊，中文名稱機器學習，本刊主要出版、報道計算機科學-COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE領域的研究動態(tài)以及在該領域取得的各方面的經驗和科研成果，介紹該領域有關本專業(yè)的最新進展，探討行業(yè)發(fā)展的思路和方法，以促進學術信息交流，提高行業(yè)發(fā)展。該刊已被國際權威數據庫SCIE收錄，為該領域相關學科的發(fā)展起到了良好的推動作用，也得到了本專業(yè)人員的廣泛認可。該刊最新影響因子為4.3，最新CiteScore 指數為11。

本刊近期中國學者發(fā)表的論文主要有：

• 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

英文介紹

Machine Learning雜志英文介紹

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.