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中文摘要：
由于极端支持向量分类机（ESVM）在对样本进行分类时并没有考虑到数据集中样本点的分布情况，对所有样本点的误差项都给予了相同的惩罚因子，使得分类器的分类效果很容易受到噪声、野值数据的干扰，针对这个问题，在ESVM的基础上提出了一种基于距离加权的极端支持向量机（WESVM）。由于不同的样本到其类中心距离的不同，因此对不同的样本给予不同的权重。分类实验结果表明WESVM与ELM、ESVM相比具有更好的分类精度。
英文摘要：
Extreme Support Vector Machine（ESVM） is susceptibly interfered by noise and outliers, because it does not take into account the distribution of the sample points and gives the same punishment factor to all of the error terms of sample points. For this problem, Weight Extreme Support Vector Machine （WESVM） is proposed on the basis of ESVM. Because the distances from different types of samples to the center of the sample are different, the different weights are assigned to the different training samples. The experiment results show that WESVM has a better classified accuracy compared with ESVM and Extreme Learning Machine.
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