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Results illustrate that the surrogate models based on LSSVR-GSA have good fitting and generalization abilities.
RSM and ANN models were developed and compared for their predictive and generalization abilities.
In comparison with the previously discussed approaches, the proposed FG-HPN is more accurate and yield significant generalization abilities.
A second experiment revealed that increasing to six the set of learned stimuli results in higher generalization abilities.
However, the generalization abilities of SVMs often rely on whether the selected kernel functions are suitable for real classification data.
Predictive and generalization abilities of the proposed models were investigated using several statistical parameters and compared with the support vector machines (SVM) method.
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Firstly, SVMs have better generalization ability.
However, SFLS still has several drawbacks, limiting its generalization ability.
Within this exploratory data, generalization ability was maintained through cross-validation.
Experimental results suggest that it might improve the generalization ability of the resulting neural network.
In order to investigate the generalization ability of the trained networks, three scenarios were considered.
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