Adding reliability to ELM forecasts by confidence intervals

Assuming that the outputs are normally distributed, only a standard deviation is needed to compute CIs of a predicted output (the predicted output itself is a mean). Our method provides CIs for ELM predictions by estimating standard deviation of a random output for a particular input sample. It shows good results on both toy and real skin segmentation datasets, and compares well with the existing Confidence-weighted ELM methods. On a toy dataset, the predicted CIs accurately represent the variable variance of outputs. On a real dataset, CIs improve the precision of a classification task at a cost of recall.