基于边界样本选择的支持向量机加速算法

针对支持向量机（Support Vector Machine，SVM）处理大规模数据集的学习时间长、泛化能力下降等问题，提出基于边界样本选择的支持向量机加速算法。首先，进行无监督的K均值聚类；然后，在各个聚簇内依照簇的混合度、支持度因素应用K近邻算法剔除非边界样本，获得最终的类别边界区域样本，参与SVM模型训练。在标准数据集上的实验结果表明，算法在保持传统支持向量机的分类泛化能力的同时，显著降低了模型训练时间。

SVM accelerated training algorithm based on border sample selection

Support Vector Machine（SVM）is a powerful instrument for solving pattern classification problem, but it is not suitable for large-scale data, due to the drawbacks of slow training speed, large computational cost and low generalization. An accurate support vector machine algorithm is proposed, which uses training samples lying close to the separation boundary. First of all, K-means clustering is performed to the initial training data, and then the boundary samples are selected in each cluster by K-nearest neighbor algorithm, two cluster factors, the degree of mixing and support, are defined to determine the boundary width. These boundary samples are then used in the training of the SVM classifier. The experiments on some benchmark datasets show that the proposed method not only makes computational complexities decreased, but also makes classification power of traditional SVM invariant.