Abstract
With the Onset of Easy Access to Supercomputers with High Amounts of Memory Available, Machine Learning Algorithms Have Continued to Increase the Resources Necessary to Perform their Data Analysis. This Paper Aims to Show Development in the Other Direction, by Showing that through the Use of a Combination of Feature Bagging and Ensembles of Extreme Learning Machines (Elms) It is Possible to Leverage Machine Learning, Without Loss of Accuracy, on Devices Where Flash Memory is Very Scarce, and Random-Access Memory (Ram) is Even Scarcer, Such as on Embedded Systems. This Novel Strategy is Called Feature Bagged Extreme Learning Machines (Fb-Elms).
Recommended Citation
K. Khan et al., "Feature Bagging and Extreme Learning Machines: Machine Learning with Severe Memory Constraints," Proceedings of the International Joint Conference on Neural Networks, article no. 9207673, Institute of Electrical and Electronics Engineers, Jul 2020.
The definitive version is available at https://doi.org/10.1109/IJCNN48605.2020.9207673
Department(s)
Engineering Management and Systems Engineering
International Standard Book Number (ISBN)
978-172816926-2
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jul 2020
