THE KEY THEOREM OF STATISTICAL LEARNING THEORY OF COMPLEX ROUGH SAMPLES CORRUPTED BY NOISE

摘要：

The key theorem plays an important role in the statistical learning theory. However, the researches about it at present mainly focus on real random variable and the samples which are supposed to be noise-free. In this paper, the definitions of complex rough variable and primary norm are introduced. Then, the definitions of the complex empirical risk functional, the complex expected risk functional, and complex empirical risk minimization principle about samples corrupted by noise are proposed. Finally, the key theorem of learning theory based on complex rough samples corrupted by noise is proposed and proved. The investigations help lay essential theoretical foundations for the systematic and comprehensive development of the statistical learning theory of complex rough samples.

关键词： Complez rough variable Primary norm Noise Complez empirical risk minimization principle The key theorem

作者: JING-FENG TIAN ZHI-MING ZHANG

作者单位: North China Electric Power University Science & Technology College, Baoding 071051, China College of Mathematics and Computer Sciences, Hebei University, Baoding 071002, China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 851-856

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）