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-计算机人工智能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SCI、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4.9，最新CiteScore 指数为7.5。

本刊近期中国学者发表的论文主要有：

• Improvement of Diffusion Models Based on Radial Basis Function Neural Networks and Consistency Regularizatio

  Author: Zhao, Jingsheng; Zheng, Jiashang; Yu, Chen; Fu, Huaijun; Zhang, Ruiqi

• Attribute Completion for Heterogeneous Graphs Based on Heterogeneous Relation Context Aware and Noisy Node Filterin

  Author: Wu, Ling; Tian, Yanru; Xu, Huajie; Chen, Weijun; Guo, Kun

• Expanding Domain Generalization Theory: Error Bound Beyond Convex Combination Assumptio

  Author: Wang, Xi; Bai, Liang; Yang, Xian; Liang, Jiye

• Boosting Efficient Experience Exchange in Sparse-Reward Multi-Agent Reinforcement Learnin

  Author: Zhang, Jianan; Xie, Zaipeng; Yang, Nuo; Jin, Juguang; Song, WenZhan

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.