中文摘要：

针对支持向量数据描述中噪声和孤立点带来的过拟合问题,提出了一种Vague集的支持向量数据描述（VFSVDD）,利用模糊k-均值聚类方法生成每个训练样本的真、假隶属度,可以精细地控制训练样本对超球面边界的影响。用UCI机器学习数据集的数据实验验证了VFSVDD的有效性。

英文摘要：

To resolve the problem of over-fitted caused by noises and outliers in support vector data description,fuzzy support vector data description based on vague sets（VFSVDD）is proposed in this paper.Fuzzy k-means clustering algorithm is employed for generating the truth-membership and false-membership,how each training example affects the boundary of hypersphere could be controlled.Test data from UCI machine learning repository are employed to evaluate the usefulness of VFSVDD.