Table 3.

The training accuracies of predicting the datasets

Datasets	SVM			ANN			RF			GMDH			RVFL-GMDH
	R	M	E	R	M	E	R	M	E	R	M	E	R	M	E
Boston	0.909	0.004	0.914	0.939	0.003	0.939	0.982	0.001	0.982	0.891	0.005	0.891	0.891	0.005	0.891
Abalone	0.562	0.006	0.578	0.566	0.006	0.566	0.926	0.001	0.926	0.533	0.006	0.533	0.578	0.006	0.562
Airfoil	0.841	0.005	0.842	0.848	0.005	0.848	0.993	0.000	0.993	0.842	0.005	0.842	0.842	0.005	0.842
Comm	0.803	0.011	0.807	0.768	0.013	0.768	0.937	0.004	0.937	0.665	0.019	0.665	0.667	0.019	0.667
CCPP Seg	0.937	0.003	0.937	0.938	0.003	0.938	0.993	0.001	0.993	0.938	0.003	0.938	0.939	0.003	0.939
Stock	0.950	0.003	0.950	0.986	0.000	0.986	0.996	0.000	0.996	0.942	0.003	0.942	0.990	0.001	0.990
Average	0.834	0.005	0.838	0.841	0.005	0.841	0.971	0.001	0.971	0.802	0.007	0.802	0.818	0.007	0.815

(R: R², M: MSE, E: E_VAR)