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Power-law committee machine.
Results indicate superiority of committee machine to its elements.
Hybrid GA-PS was capable of optimizing MSE of prediction for committee machine such that performance function of committee machine is located in its global minimum.
Assuming average method as the simplest committee machine, following equation results in outputs of committee machine: O CM = 1 K ∑ i = 1 K O i (5).
To improve model performance, here we tested an approach with the committee machine.
In this study, an ensemble-based committee machine with empirical formulas (CMEF) is used.
To develop the committee machine, a hybrid GA-PS tool is embedded in its structure.
mAIS: The number of benign apps our committee machine was able to unsuccessfully classify.
Small values of mean and standard deviation (STD) reveal high performance of committee machine modeling.
In the other hand, committee machine by little additional computation enhances accuracy of final prediction, significantly.
Similar(1)
A novel predictive method, the so-called Power-Law constituentschine (PLCM) with cofSVRtuents of SVR and ACE, was then employed for estimation of the amount of asphaltene precipitation.
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