会议专题

SOME THEORETICAL STUDIES ON LEARNING THEORY WITH SAMPLES CORRUPTED BY NOISE

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.

Statistical learning theory noise expected risk functional empirical risk functional ERM principle

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

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

国际会议

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

大连

英文

3480-3485

2006-08-13(万方平台首次上网日期,不代表论文的发表时间)