Abstract: The accuracy of classification of SVM in a two-class classification problem would be decreased because of those promiscuous samples.KCNN-SVM is proposed in this paper as an improved NN-SVM algorithm，which prunes a sample according to their nearest neighbor’s class label as well as the average distance in kernel space between it and its k congener nearest neighbors.Experimental results show that KCNN-SVM algorithm is better than both SVM and NN-SVM in accuracy of classification and the total training and testing time is comparative to that of NN-SVM.

Key words: Support Vector Machine, kernel space, text categorization

摘要： 支持向量机在处理两类分类问题时，当两类样本混杂严重时会降低分类精度。在NN-SVM分类算法的基础上，通过计算样本点与其最近邻点类别的异同以及该点与其k个同类近邻点在核空间的平均距离修剪混淆点，进而提出了一种改进的NN-SVM算法——KCNN-SVM。实验数据表明，KCNN-SVM算法与SVM以及NN-SVM相比，有着更高的分类精度和更快的训练、分类时间。

关键词: 支持向量机, 核空间, 文本分类