Review of Optimization in Improving Extreme Learning Machine

Rathod, Nilesh and Wankhade, Sunil (2021) Review of Optimization in Improving Extreme Learning Machine. EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, 8 (28). e2. ISSN 2410-0218

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Abstract

Now a days Extreme Learning Machine has gained a lot of interest because of its noteworthy qualities over single hidden-layer feedforward neural networks and the kernel functions. Even if ELM has many advantages, it has some potential shortcomings such as performance sensitivity to the underlying state of the hidden neurons, input weights and the choice of functions of activation. To overcome the limitations of traditional ELM, analysts have devised numerical methods to optimise specific parts of ELM in order to enhance ELM performance for a variety of complicated difficulties and applications. Hence through this study, we intend to study the different algorithms developed for optimizing the ELM to enhance its performance in the aspects of survey criteria such as datasets, algorithm, objectives, training time, accuracy, error rate and the hidden neurons. This study will help other researchers to find out the research issues that lowering the performance of the ELM.

Item Type: Article
Uncontrolled Keywords: Extreme learning machine (ELM), Single-feedforward neural networks; Kernel functions, Sensitivity, Input weights and Activation bias
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
T Technology > T Technology (General)
Depositing User: EAI Editor IV
Date Deposited: 29 Sep 2021 12:51
Last Modified: 29 Sep 2021 12:51
URI: https://eprints.eudl.eu/id/eprint/7137

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