雜志介紹

Machine Learning雜志介紹

《Machine Learning》是一本以English為主的未開(kāi)放獲取國(guó)際優(yōu)秀期刊，中文名稱機(jī)器學(xué)習(xí)，本刊主要出版、報(bào)道計(jì)算機(jī)科學(xué)-COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE領(lǐng)域的研究動(dòng)態(tài)以及在該領(lǐng)域取得的各方面的經(jīng)驗(yàn)和科研成果，介紹該領(lǐng)域有關(guān)本專業(yè)的最新進(jìn)展，探討行業(yè)發(fā)展的思路和方法，以促進(jìn)學(xué)術(shù)信息交流，提高行業(yè)發(fā)展。該刊已被國(guó)際權(quán)威數(shù)據(jù)庫(kù)SCIE收錄，為該領(lǐng)域相關(guān)學(xué)科的發(fā)展起到了良好的推動(dòng)作用，也得到了本專業(yè)人員的廣泛認(rèn)可。該刊最新影響因子為4.3，最新CiteScore 指數(shù)為11。

本刊近期中國(guó)學(xué)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.