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A novel randomized machine learning approach: Reservoir computing extreme learning machine
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DOI：10.1016/j.asoc.2020.106433.png)
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Abstract
En 中文
In this study, a novel approach that is based on reservoir computing, which is a successful method in modeling sequential datasets, and extreme learning machines, which has a high generalization capacity, was proposed to model a non-sequential dataset or system. The proposed approach does not require any optimization stage; each weight (except weights in the output layer), biases, the number of neurons in the reservoir, activation functions and the parameters of activation functions were determined arbitrarily and the weights in the output layer were calculated based on these arbitrarily assigned parameters. The proposed approach was evaluated and validated with 60 different benchmark datasets. Obtained results were compared with literature findings and results obtained by each of the extreme learning machine (ELM), randomized artificial neural network, random vector functional link, stochastic ELM, and pruned stochastic ELM methods. Achieved results are successful enough to be employed in classification and regression. (C) 2020 Elsevier B.V. All rights reserved.
Keywords:
Reservoir computing
Extreme learning machine
Randomization in machine learning
Randomized artificial neural network
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