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From Table 2, we could find that these selected kernel functions perform well, but the generalization performance of SVM with nonlinear kernel such as RBF and Poly is superior to that of SVM with linear kernel.
We performed experiments to explore the generalization performance of hypernetwork learning in this data set by two-fold cross validation.
Fair estimation of the generalization performance.
In this section, we evaluate the generalization performance of BFSD.
This clearly demonstrates the generalization performance of our proposed method and robustness to dataset biases.
Then, the hidden neurons of RSONN can be added or pruned to improve the generalization performance.
The generalization performance of SVM classifier is greatly influenced by the proper setting of its parameters.
If training input points and models are simultaneously optimized, the generalization performance would be further improved.
The hold-out method has the advantage to confirm the generalization performance of the finally chosen model.
Therefore, the ensemble classifier based on SVM component classifier could boost the generalization performance through regulating the parameters reasonably.
Besides, it was confirmed that the error estimates in the outer loop yielded realistic estimates of the generalization performance.
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