计算机应用与软件Issue(4)：42-45,58,5.DOI:10.3969/j.issn.1000-386x.2015.04.010
GOS＋MU：一种查询对象选择新方法
GOS+MU:A NEW METHOD OF QUERY OBJECT SELECTION
摘要
Abstract
After analysing the defects of single MU (most uncertainty)sampling,we put forward a GOS (global optimum search)method and combines MU method with it to jointly implement the query selection.In GOS +MU method,GOS focuses on searching the object globally,under the conditions of limited training samples provided by the application environment and insufficient classifier training,the object selected by this method has high learning value and can fast promote the learning process of classifier;and MU can selects the samples with most uncertainty to supplement training set using current training outcomes of classifier when the GOS fails in sampling.By the simulation on classifying users’reviews on networks products and comparing the effects of other sampling learning methods,the effectiveness of GOS+MU method in compressing the learning cost and improving the training efficiency has been proved.关键词
查询选择/不确定性采样/条件熵/全局最优搜索/采样阈值Key words
Query selection/Uncertainty sampling/Conditional entropy/Global optimum search/Sampling threshold分类
信息技术与安全科学引用本文复制引用
陈念,唐振民,帅小应..GOS＋MU：一种查询对象选择新方法[J].计算机应用与软件,2015,(4):42-45,58,5.基金项目
安徽省教育厅自然重点项目（KJ2012A211）。陈念，副教授，主研领域机器学习与人工智能。 （）
