Another method of RBF neural networks attributions selection based on a separability criterion ranking is presented in this paper.
提出了一种可分性判据排序的RBF神经网络属性选择方法。
It avoids the deficiency of traditional neural network methods needing to train all attributions, which greatly improves the efficiency of attributions selection.
该方法避免了现有的神经网络降维方法必须对全部属性进行训练和裁剪的弊端，大大提高了属性选择的效率。
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