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
Simplified neutrosophic harmonic averaging projection-based method for multiple attribute decision-making problems
Jun Ye (June 2017)
State estimation for uncertain discrete-time stochastic neural networks with Markovian jump parameters and time-varying delays
- Journal Title
- International Journal of Machine Learning and Cybernetics
- Volume 1 / 2010 - Volume 8 / 2017
- Print ISSN
- Online ISSN
- Springer Berlin Heidelberg
- Additional Links
- Industry Sectors
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