Abstract: Since SVM is very sensitive to outliers and noises in the training set,a support vector machine algorithm based on training repeatedly is proposed in this paper.Samples having effects on decision surface after being trained repeatedly are chosen.And then they are trained repeatedly for some times according to their fuzzy membership.The weight of these samples is changed by this way and reduced in the influence of outliers and noises.The improved SVM algorithm is employed to text categorization,though the training time is increased,better effect is obtained than the traditional support vector machine,and this method effectively distinguishes between the valid samples and the outliers or noises．

摘要： 针对支持向量机中存在的对噪音和野值敏感的问题,提出了一种基于重复训练的支持向量机方法。该方法选取重复训练后会对分类面有影响的样本,根据其类别隶属度,重复训练相应的次数,以此来改变样本的权值,减小噪音和野值的影响。将该算法应用于文本分类中,实验结果表明,该方法在适度增加了训练时间的情况下,不仅比标准支持向量机方法具有更好的抗噪音和野值的能力,而且提高了分类性能。