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.
Recommended Citation
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
Department(s)
Electrical and Computer Engineering
Research Center/Lab(s)
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)
2168-2216
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2017 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Oct 2017
