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Multilayer extreme learning machine: a systematic review

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Abstract

Majority of the learning algorithms used for the training of feedforward neural networks (FNNs), such as backpropagation (BP), conjugate gradient method, etc. rely on the traditional gradient method. Such algorithms have a few drawbacks, including slow convergence, sensitivity to noisy data, local minimum problem, etc. One of the alternatives to overcome such issues is Extreme Learning Machine (ELM), which requires less training time, ensures global optimum and enhanced generalization in neural networks. ELM has a single hidden layer, which poses memory constraints in some problem domains. An extension to ELM, Multilayer ELM (ML-ELM) performs unsupervised learning by utilizing ELM autoencoders and eliminates the need of parameter tuning, enabling better representation learning as it consists of multiple layers. This paper provides a thorough review of ML-ELM architecture development and its variants and applications. The state-of-the-art comparative analysis between ML-ELM and other machine and deep learning classifiers demonstrate the efficacy of ML-ELM in the niche domains of Computer Science which further justifies its competency and effectiveness.

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Acknowledgements

The authors acknowledge the financial support provided by the Department of Science and Technology (DST), Government of India under Innovation in Science Pursuit for Inspired Research (INSPIRE) Fellowship, INSPIRE Code- IF190242, for carrying out this research.

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  1. Department of Computer Science and Engineering, Thapar Institute of Engineering & Technology, Patiala, 147004, Punjab, India

    Ravneet Kaur, Rajendra Kumar Roul & Shalini Batra

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Kaur, R., Roul, R.K. & Batra, S. Multilayer extreme learning machine: a systematic review. Multimed Tools Appl 82, 40269–40307 (2023). https://doi.org/10.1007/s11042-023-14634-4

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