河北大学学报(自然科学版) ›› 2016, Vol. 36 ›› Issue (2): 113-116.DOI: 10.3969/j.issn.1000-1565.2016.02.001

• •    下一篇

基于Hybrid样本的学习过程一致收敛速度的界

李俊华1,白鹤举2   

  • 收稿日期:2015-07-01 出版日期:2016-03-25 发布日期:2016-03-25
  • 作者简介:李俊华(1979—),女,河北衡水人,河北大学讲师,主要从事不确定统计学习理论研究. E-mail:junhuali2008@126.com
  • 基金资助:
    国家自然科学基金资助项目(11201110);河北省教育厅资助项目(QN20131055)

Bounds on the rate of uniform convergence of learning process based on Hybrid samples

LI Junhua1,BAI Heju2   

  1. 1.College of Mathematics and Information Science, Hebei University, Baoding 071002, China; 2.Basic Teaching Department, Chengde Petroleum College, Chengde 067000, China
  • Received:2015-07-01 Online:2016-03-25 Published:2016-03-25

摘要: 学习过程收敛速度的界是统计学习理论的重要组成部分,这些界决定了学习机器的推广能力.以机会理论和Hybrid变量的概念为基础,讨论了基于Hybrid样本的学习过程一致收敛速度的界,并给出了这些界和函数容量之间的关系.

关键词: Hybrid变量, Hybrid经验风险最小化原则, 一致收敛速度的界

Abstract: Bounds on the rate of uniform convergence of learning process are important component part of statistical learning theory and the bounds determine the generalization abilities of learning machines.Based on the chance theory and the definition of Hybrid variable,bounds on the rate of uniform convergence of learning process based on hybrid samples are discussed and the relationship between the bounds and the capacity of the set of functions is given.

Key words: Hybrid variable, Hybrid empirical risk minimization principle, the rate of uniform convergence

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