计算机技术与发展2017，Vol.27Issue(10)：43-48,6.DOI:10.3969/j.issn.1673-629X.2017.10.010
典型半监督分类算法的研究分析
Research and Analysis of Typical Semi-supervised Classification Algorithm
摘要
Abstract
Large amounts of semi-supervised classification algorithms have been proposed recently,however,it is really hard to decide which one to use in real learning tasks,and further there is no related guidance in literature. Therefore,empirical comparisons of several typical algorithms have been performed to provide some useful suggestions. In fact,semi-supervised classification algorithms can be cate-gorized by the data distribution assumption. Therefore,typical algorithms with different assumption adoptions have been contrasted. Spe-cifically,they are Transductive Support Vector Machine (TSVM) using the cluster assumption,Laplacian Regularized Least Squares Classification ( LapRLSC) using the manifold assumption, and SemiBoost using both assumptions, and Implicitly Constrained Least Squares ( ICLS) without any assumption,with the supervised least Squares Classification ( LS) as the base line. Eventually it is conclu-ded that when data distribution is given,the semi-supervised classification algorithm that adopts corresponding assumption can lead to the best performance;without any prior knowledge about data distribution,TSVM can be a good choice when the given labeled samples are extremely limited;when the labeled samples are not so scarce,and meanwhile if learning safety is emphasized,ICLS is proposed,and La-pRLSC is another good choice.关键词
半监督分类/数据分布/聚类假设/流行假设Key words
semi-supervised classification/data distribution/cluster assumption/manifold assumption分类
信息技术与安全科学引用本文复制引用
孟岩,汪云云..典型半监督分类算法的研究分析[J].计算机技术与发展,2017,27(10):43-48,6.基金项目
国家自然科学基金资助项目(61300165) （61300165）
高等学校博士学科点专项科研基金新教师类(20133223120009) （20133223120009）
南京邮电大学引进人才基金(NY213033) （NY213033）
