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.
F. Sun et al., "Efficient and Rapid Machine Learning Algorithms for Big Data and Dynamic Varying Systems," IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 47, no. 10, pp. 2625 - 2626, Institute of Electrical and Electronics Engineers (IEEE), Oct 2017.
The definitive version is available at https://doi.org/10.1109/TSMC.2017.2741558
Electrical and Computer Engineering
Intelligent Systems Center
Second Research Center/Lab
Center for High Performance Computing Research
Keywords and Phrases
Artificial intelligence; Big data; Learning systems; Computational intelligence techniques; Convergence time; Exponential growth; Fast implementation; Human intervention; Latest development; Machine learning techniques; Research challenges; Learning algorithms
International Standard Serial Number (ISSN)
Article - Journal
© 2017 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 Oct 2017