计算机技术与发展Issue(5)：109-112,116,5.DOI:10.3969/j.issn.1673-629X.2014.05.026
基于图的组合半监督SVM聚类核算法研究
Research on Combined Semi-supervised SVM Cluster Kernel Algorithm Based on Graph
摘要
Abstract
In order to further improve the classification accuracy of SVM based on the cluster assumption,represent the similarity matrix of the weighted undirected graph by linear-step transfer function to establish a cluster kernel based on graph,which alters the map distance metric,so the distance between two points in the same cluster is smaller. Combining it linearly to the polynomial kernel function,a com-bined cluster kernel for semi-supervised SVM based on graph is constructed. Then train support vector machine with it and obtain the classification accuracy. Experiments show that,compared with the standard SVM algorithm,the classification accuracy of the proposed al-gorithm is higher,and better than the individual ones. With the increase in the proportion of labeled samples,the classification accuracy of this algorithm is also increasing,using the information of unlabeled samples effectively.关键词
半监督支持向量机/聚类核/图/分类Key words
S3 VM/cluster kernel/graph/classification分类
信息技术与安全科学引用本文复制引用
郑文静,李雷..基于图的组合半监督SVM聚类核算法研究[J].计算机技术与发展,2014,(5):109-112,116,5.基金项目
国家自然科学基金资助项目(61070234,61071167) （61070234,61071167）
