Title
Efficient and Rapid Machine Learning Algorithms for Big Data and Dynamic Varying Systems.
Abstract
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.
Year
DOI
Venue
2017
10.1109/TSMC.2017.2741558
IEEE Trans. Systems, Man, and Cybernetics: Systems
Keywords
Field
DocType
Big Data,Feature extraction,Support vector machines,Cybernetics,Acceleration,Kernel,Delays
Instance-based learning,Active learning (machine learning),Computer science,Hyper-heuristic,Artificial intelligence,Computational learning theory,Online machine learning,Mathematical optimization,Stability (learning theory),Computational intelligence,Algorithm,Big data,Machine learning
Journal
Volume
Issue
ISSN
47
10
2168-2216
Citations 
PageRank 
References 
4
0.41
0
Authors
5
Name
Order
Citations
PageRank
Fuchun Sun12377225.80
Guang-Bin Huang211303470.52
Q. M. Jonathan Wu32457164.07
Shiji Song4124794.76
Wunsch II Donald C.5135491.73