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 systemsPattern Recognition technology to support discovery of system-environment interactionControl of system-environment interactionsBiochemical interaction in biological and biologically-inspired systemsLearning for improvement of communication schemes between systems
控制论涉及到描述我们日常生活中无所不在的系统之间复杂的相互作用和相互关系。机器学习发现系统中变量和变量集合之间的基本函数关系。机器学习和控制论学科的融合旨在通过不同的数据学习机制发现系统之间的各种形式的交互作用。国际机器学习与控制论杂志（IJMLC）关注机器学习与控制论结合处出现的关键研究问题，并作为快速传播该领域最新进展的广泛论坛。IJMLC的重点是机器学习和控制论方案的混合开发，这些方案受工程、数学、认知科学和应用等不同贡献学科的启发。与机器学习和控制论所有方面相关的新思想、设计备选方案、实现和案例研究属于IJMLC的范围。该期刊将涵盖的主要研究领域包括：用于系统间交互建模的机器学习支持系统环境交互发现的模式识别技术系统环境交互控制生物和生物激发系统中的生物化学相互作用学习改进系统间通信方案
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