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This paper proposes a way of providing transparent and interpretable results for ELM models by adding confidence intervals to the predicted outputs. In supervised learning, outputs are often random variables because they may depend on information that is unavailable, due to the presence of noise, or the projection function itself may be stochastic. Probability distribution of outputs is input dependent,...
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