International Journal of Machine Learning and Cybernetics

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

International Journal of Machine Learning and Cybernetics（IJMLC）专注于机器学习和控制论交叉领域的关键研究问题，提供了一个快速传播该领域最新进展的广泛论坛。该期刊着重于机器学习和控制论方案的综合发展，这些方案受到工程学、数学、认知科学和应用等不同学科的启发。与机器学习和控制论所有方面相关的新思想、设计模式、实施案例和研究都属于IJMLC的范畴。 该期刊涵盖的主要研究领域包括： - 用于系统间互动建模的机器学习技术； - 支持发现系统与环境相互作用模式的模式识别技术； - 系统与环境相互作用的控制策略； - 生物系统和生物启发系统中的生化相互作用； - 改进系统间通信方案的学习算法。 IJMLC旨在促进这些领域的学术交流，推动相关理论和实践的进步。