SOME THEORETICAL STUDIES ON LEARNING THEORY WITH SAMPLES CORRUPTED BY NOISE

摘要：

In statistical learning theory (SLT), the key theorem and the bounds on the rate of uniform convergence of learning processes provide theoretical basis for the applied research of support vector machine etc., so they play important roles in SLT. In the study of two aspects,samples which we deal with are supposed to be noise-free.But it is not always the case because of the influence of human or environmental factors. With a view of this, we propose and prove the key theorem and discuss the bounds on the rate of uniform convergence of learning processes when samples are corrupted by noise.

关键词： Statistical learning theory noise expected risk functional empirical risk functional ERM principle

作者: JUN-HUA LI MING-HU HA YUN-CHAO BAI JING TIAN

作者单位: College of Mathematical and Computer Sciences, Hebei University Baoding 071002, China College of Economy, Hebei University Baoding 071002, China College of Quality and Technical Supervision, Hebei University, Baoding 071002, China

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 3480-3485

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）