An Enhanced Category Detection Based on Active Learning

摘要：

Identication of useful anomalies is an emerging task in active learning scenario. It plays the central roles in category detection in which one can using a sampling approach to label a data from rare category in an unlabeled date set by the help of the oracle who has a small querying budget. This paper presents an enhanced category detection that improves previous research work which leans to cost more querying budget. The new approach takes full advantage of the feedback of the oracle, and reduces the querying times. Experimental results on both synthetic and real data sets are effective and low-cost.

作者: Hao Huang Shuoping Wang Lianhang Ma

作者单位: College of Computer Science and Technology, Zhejiang University, Hangzhou, 310027, China Computer Science School, Zhejiang University City College, Hangzhou, 310015, China

会议类型: 国际会议

会议名称: The 2010 International Conference on Intelligent Systems and Knowledge Engineering(第五届智能系统与知识工程国际会议)

会议地点: 杭州

会议语种:英文

页码: 224-227

在线出版日期: 2010-11-15（万方平台首次上网日期，不代表论文的发表时间）