首页> 外文会议>2010 International Conference on Multimedia Technology >The Proof of Linear Function Set's VC Dimension and Its Application
【24h】

The Proof of Linear Function Set's VC Dimension and Its Application

机译:线性函数集的VC维证明及其应用

获取原文

摘要

Statistical learning theory is the most important theory in statistical estimation and forecasting of small samples. VC dimension and structural risk minimization principle are important concepts of statistical learning theory. This article firstly proves the situation of linear indicator function set's VC dimension in n-dimensional space with algebraic method. Then, in the specific instances of handwritten number recognition, we discussed the effect of features number on classification accuracy rate with the tools of perceptron algorithm in pattern recognition and the linear function's VC dimension and the structural risk minimization principle.
机译:统计学习理论是统计估算和对小样本预测中最重要的理论。 VC维度和结构风险最小化原则是统计学习理论的重要概念。本文首先通过代数方法证明了线性指示器功能集的线性指标功能集的vc尺寸的情况。然后,在手写号码识别的具体实例中,我们讨论了在图案识别和线性函数的VC维度中的Perceptron算法和结构风险最小化原理的Perceptron算法对分类精度率的影响。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号