Efficient and Rapid Machine Learning Algorithms for Big Data and Dynamic Varying Systems

With the exponential growth of data and complexity of systems, fast machine learning/artificial intelligence and computational intelligence techniques are highly required. Many conventional computational intelligence techniques face bottlenecks in learning (e.g., intensive human intervention and convergence time) [item 1) in the Appendix]. However, efficient learning algorithms alternatively offer significant benefits including fast learning speed, ease of implementation, and minimal human intervention. The need for efficient and fast implementation of machine learning techniques in big data and dynamic varying systems poses many research challenges. This special issue highlights some latest development in the related areas.