In extreme learning machine (ELM) framework, the hidden layer setting determines its generalization ability; and in presence of outliers training set, weights between output based on least squares would be overly estimated. To address these two problems ELM implementation, we extend robust penalized statistical framework propose a general ELM, which consists components (robust loss function reg...

A smoothing principle for M-estimators is proposed. The smoothing depends on the sample size so that the resulting smoothed M-estimator coincides with the initial M-estimator when n → ∞. The smoothing principle is motivated by an analysis of the requirements in the proof of the Cramér-Rao bound. The principle can be applied to every M-estimator. A simulation study is carried out where smoothed ...

We have developed a performance prediction model for non-bonded interaction computations in molecular dynamics simulations, thereby predicting the optimal cell dimension in a linked-list cell method. The model expresses computation time in terms of the number and unit computation time of key operations. The model accurately estimates the number of operations during the simulations with the maxi...