Abstract: Although Twin Support Vector Machine（TSVM） has faster speed than traditional support vector machine for classification problem, it does not take the importance of the training samples on the learning of the decision hyperplane into account with respect to the classification task. In this paper, Fuzzy Twin Support Vector Machine（FTSVM） is proposed by applying a fuzzy membership to each training sample to reduce the effects of the samples on the hyperplane. Experiments on several UCI benchmark datasets show that the fuzzy twin support vector machine is effective and feasible relative to twin support vector machine, fuzzy support vector machine and support vector machine.

Key words: Twin Support Vector Machine, fuzzy weighting, classification

摘要： 虽然孪生支持向量机（Twin Support Vector Machine，TSVM）的处理速度优于传统的支持向量机，但其并没有考虑输入样本点对最优分类超平面所产生的不同影响。通过为每个训练样本赋予不同的样本重要性，以及减少样本点对非平行超平面的影响，提出了模糊加权孪生支持向量机（Fuzzy TSVM，FTSVM）。在UCI标准数据集上，对FTSVM进行了实验研究并与TSVM、FSVM和SVM方法进行了比较，实验结果表明FTSVM方法是有效的。

关键词: 孪生支持向量机, 模糊加权, 分类