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Extreme Learning Machine (ELM), which was initially proposed for training single-layer feed-forward networks (SLFNs), provides us a unified efficient and effective framework for regression and multiclass classification. Though various ELM variants were proposed in recent years, most of them focused on the supervised learning scenario while little effort was made to extend it into unsupervised learning...
Extreme learning machine (ELM) uses a non-iterative method to train single-hidden-layer feed-forward networks (SLFNs), which has been proven to be an efficient and effective learning model for both classification and regression. The main advantage of ELM lies in that the input weights as well as the hidden layer biases can be randomly generated, which contributes to the analytical solution of output...
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