基于UCI数据集的仿真结果表明，NFS - RSVM方法能有效地将数据中的大部分噪声点去除，与传统的SVM和FSVM相比分类精度有一定程度的提高。
The simulation results on UCI show that NFS-RSVM can remove most of the noises effectively, and the accuracy is improved partly compared with the traditional SVM and FSVM.
首先，为了去除测量产生的噪声和误差，引入高斯核函数为每个采样点加权；
To filter out the noise and error arising out of various physical measurement processes and limitations of the acquisition technology, a Gaussian weight is assigned to each point acquired.
应用基于样本之间的紧密度确定每个样本的模糊隶属度，通过训练确定阀值，去除影响得到最优分类超平面的噪声和野点。
The fuzzy membership of each sample is defined by affinity among samples, and by the training determine a threshold, noises and outliers are removed, which influence optimal separating hyperplane.
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