控制論涉及到描述我們?nèi)粘Ｉ钪袩o所不在的系統(tǒng)之間復(fù)雜的相互作用和相互關(guān)系。機器學(xué)習(xí)發(fā)現(xiàn)系統(tǒng)中變量和變量集合之間的基本函數(shù)關(guān)系。機器學(xué)習(xí)和控制論學(xué)科的融合旨在通過不同的數(shù)據(jù)學(xué)習(xí)機制發(fā)現(xiàn)系統(tǒng)之間的各種形式的交互作用。國際機器學(xué)習(xí)與控制論雜志（IJMLC）關(guān)注機器學(xué)習(xí)與控制論結(jié)合處出現(xiàn)的關(guān)鍵研究問題，并作為快速傳播該領(lǐng)域最新進展的廣泛論壇。IJMLC的重點是機器學(xué)習(xí)和控制論方案的混合開發(fā)，這些方案受工程、數(shù)學(xué)、認知科學(xué)和應(yīng)用等不同貢獻學(xué)科的啟發(fā)。與機器學(xué)習(xí)和控制論所有方面相關(guān)的新思想、設(shè)計備選方案、實現(xiàn)和案例研究屬于IJMLC的范圍。該期刊將涵蓋的主要研究領(lǐng)域包括：用于系統(tǒng)間交互建模的機器學(xué)習(xí)支持系統(tǒng)環(huán)境交互發(fā)現(xiàn)的模式識別技術(shù)系統(tǒng)環(huán)境交互控制生物和生物激發(fā)系統(tǒng)中的生物化學(xué)相互作用學(xué)習(xí)改進系統(tǒng)間通信方案

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