渭南师范学院学报：综合版Issue(2)：83-86,4.
基于改进的K近邻算法支持向量分类研究
Research on Support Vector Classification Based on Improved K-Nearest Neighbor Algorithm
摘要
Abstract
Traditional Support Vector Machine （SVM） algorithms for classification optimize all support vectors in the optimiza- tion process. They increase the amount of computation and reduce the efficiency of training. Aiming at the above shortcomings, it a- dopts an improved K-nearest neighbor algorithm to assign affiliation to known samples on the basis of analysis sample fuzzy affilia- tion, which eliminates the non - support vector and reduces the training samples. This method was used for classification of Chinese web pages and obtained better classification results. The simulation results show that the improved approach is not only simpleness, but also can effectively reduce the number of training samples of SVM and enhance the training and testing speed of SVM in the case of ensuring classifier performance.关键词
K近邻算法/支持向量/分类Key words
K- nearest neighbor algorithm/support vector/classification分类
信息技术与安全科学引用本文复制引用
林关成..基于改进的K近邻算法支持向量分类研究[J].渭南师范学院学报：综合版,2012,(2):83-86,4.基金项目
基金项目：陕西省教育厅科研资助项目 （）
