The Bounds on the Rate of Uniform Convergence of Learning Process with Rough Samples

摘要：

Support vector machine is a research hotspot in the area of machine learning, and the bounds on the rate of uniform convergence of statistical learning theory describe the extended ability of learning machine based on ERM. In the paper, Rough Empirical Risk Minimization (RERM) principle is proposed, and the bounds on the rate of uniform convergence of learning process with rough samples are presented and proven, they provide a theoretical basis for the research of rough support vector machine. Which has a wide range of applications in Natural Language Processing, including automatic summarization, text classification, etc.

关键词： SVM Rough samples The bounds

作者: Hu Shicheng Xu Yongdong Liu Yang

作者单位: School of Computer Science and Technology, Harbin Institute of Technology at Weihai, Weihai,Shandong,264209,China

会议类型: 国际会议

会议名称: 2010 International Conference on Intelligent Computation Technology and Automation(2010 智能计算技术与自动化国际会议 ICICTA 2010)

会议地点: 长沙

会议语种:英文

页码: 3084-3087

在线出版日期: 2010-05-11（万方平台首次上网日期，不代表论文的发表时间）