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Training of an extreme learning machine autoencoder based on an iterative shrinkage-thresholding optimization algorithm
(2022)
Orthogonal transformations, proper decomposition, and the Moore–Penrose inverse are traditional methods of obtaining the output layer weights for an extreme learning machine autoencoder. However, an increase in the number ...
A comprehensive review of extreme learning machine on medical imaging
(2023)
The feedforward neural network based on randomization has been of great interest in the scientific community, particularly extreme learning machines, due to its simplicity, training speed, and levels of accuracy comparable ...