Journal of Soft Computing Paradigm
IRO Journals
Volume 3 — Issue 2 — June 2021

Study of Variants of Extreme Learning Machine (ELM) Brands and its Performance Measure on Classification Algorithm

https://doi.org/10.36548/jscp.2021.2.003
J. Samuel Manoharan
Professor/Dept. of ECE, Sir Isaac Newton College of Engineering and Technology, Papakoil, Nagapattinam – 611102, TamilNadu, India
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Manoharan, J. Samuel. 2021. “Study of Variants of Extreme Learning Machine (ELM) Brands and Its Performance Measure on Classification Algorithm”. Journal of Soft Computing Paradigm 3 (2): 83-95. https://doi.org/10.36548/jscp.2021.2.003.
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Keywords

— Extreme Learning Machine
Published: 07-06-2021

Abstract

Recently, the feed-forward neural network is functioning with slow computation time and increased gain. The weight vector and biases in the neural network can be tuned based on performing intelligent assignment for simple generalized operation. This drawback of FFNN is solved by using various ELM algorithms based on the applications issues. ELM algorithms have redesigned the existing neural networks with network components such as hidden nodes, weights, and biases. The selection of hidden nodes is randomly determined and leverages good accuracy than conservative methods. The main aim of this research article is to explain variants of ELM advances for different applications. This procedure can be improved and optimized by using the neural network with novel feed-forward algorithm. The nodes will mainly perform due to the above factors, which are tuning for inverse operation. The ELM essence should be incorporated to reach a faster learning speed and less computation time with minimum human intervention. This research article consists of the real essence of ELM and a briefly explained algorithm for classification purpose. This research article provides clear information on the variants of ELM for different classification tasks. Finally, this research article has discussed the future extension of ELM for several applications based on the function approximation.

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