Improved Margin Sampling for Active Learning

摘要：

　　Active learning is a learning mechanism which can actively query the user for labels.The goal of an active learning algorithm is to build an effective training set by selecting those most informative samples and improve the efficiency of the model within the limited time and resource.In this paper,we mainly focus on a state-of-the-art active learning method,the SVM-based margin sampling.However,margin sampling does not consider the distribution and the structural space connectivity among the unlabeled data when several examples are chosen simultaneously,which may lead to oversampling on dense regions.To overcome this shortcoming,we propose an improved margin sampling method by applying the manifold-preserving graph reduction algorithm to the original margin sampling method.Experimental results on multiple data sets demonstrate that our method obtains better classification performance compared with the original margin sampling.

关键词： Active learning Margin sampling Support vector machine Manifold-preserving graph reduction

作者: Jin Zhou Shiliang Sun

作者单位: Department of Computer Science and Technology East China Normal University 500 Dongchuan Road,Shanghai 200241,China

会议类型: 国际会议

会议名称: Chinese Conference on Pattern Recognition, CCPR(2014年全国模式识别学术会议)

会议地点: 长沙

会议语种:英文

页码: 120-129

在线出版日期: 2014-11-01（万方平台首次上网日期，不代表论文的发表时间）