雜志介紹

International Journal Of Machine Learning And Cybernetics雜志介紹

《International Journal Of Machine Learning And Cybernetics》是一本以English為主的未開放獲取國際優(yōu)秀期刊，中文名稱國際機器學習與控制論雜志，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(jīng)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(quán)威數(shù)據(jù)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3.1，最新CiteScore 指數(shù)為7.9。

本刊近期中國學者發(fā)表的論文主要有：

• Learning relations in human-like style for few-shot fine-grained image classification

  Author: Li, Shenming; Feng, Lin; Xue, Linsong; Wang, Yifan; Wang, Dong

• Locality-constrained weighted collaborative-competitive representation for classification

  Author: Gou, Jianping; Xiong, Xiangshuo; Wu, Hongwei; Du, Lan; Zeng, Shaoning; Yuan, Yunhao; Ou, Weihua

• Distance metric learning with local multiple kernel embedding

  Author: Zhang, Qingshuo; Tsang, Eric C. C.; He, Qiang; Hu, Meng

• Micro-extended belief rule-based system with activation factor and parameter optimization for industrial cost prediction

  Author: Wang, Suhui; Ye, Fei-Fei; Yang, Long-Hao; Liu, Jun; Wang, Hui; Martinez, Luis

英文介紹

International Journal Of Machine Learning And Cybernetics雜志英文介紹

Cybernetics is concerned with describing complex interactions and interrelationships between systems which are omnipresent in our daily life. Machine Learning discovers fundamental functional relationships between variables and ensembles of variables in systems. The merging of the disciplines of Machine Learning and Cybernetics is aimed at the discovery of various forms of interaction between systems through diverse mechanisms of learning from data.

The International Journal of Machine Learning and Cybernetics (IJMLC) focuses on the key research problems emerging at the junction of machine learning and cybernetics and serves as a broad forum for rapid dissemination of the latest advancements in the area. The emphasis of IJMLC is on the hybrid development of machine learning and cybernetics schemes inspired by different contributing disciplines such as engineering, mathematics, cognitive sciences, and applications. New ideas, design alternatives, implementations and case studies pertaining to all the aspects of machine learning and cybernetics fall within the scope of the IJMLC.

Key research areas to be covered by the journal include:

Machine Learning for modeling interactions between systems

Pattern Recognition technology to support discovery of system-environment interaction

Control of system-environment interactions

Biochemical interaction in biological and biologically-inspired systems

Learning for improvement of communication schemes between systems