Learning Machine Based on Differential Capacity Control

ZHANG Li; ZHOU Wei-da; JIAO Li-cheng

您当前的位置：

首页 >

文章列表页 >

Learning Machine Based on Differential Capacity Control

更新时间：2025-07-16

- Learning Machine Based on Differential Capacity Control
- Acta Electronica Sinica Vol. 31, Issue 10, Pages: 1526-1531(2003)
- 作者机构：
  
  西安电子科技大学雷达信号处理重点实验室,陕西,西安,710071
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP391.4
- Published Online：25 October 2003，
  
  Published：2003
- 稿件说明：
移动端阅览
ZHANG Li, ZHOU Wei-da, JIAO Li-cheng. Learning Machine Based on Differential Capacity Control[J]. Acta Electronica Sinica, 2003, 31(10): 1526-1531.
DOI：

ZHANG Li, ZHOU Wei-da, JIAO Li-cheng. Learning Machine Based on Differential Capacity Control[J]. Acta Electronica Sinica, 2003, 31(10): 1526-1531. DOI：

摘要

本文通过对函数集容量的分析

得出用函数的微分来控制函数集容量的学习方法.该方法不仅能用支撑矢量核函数而且可以采用其他的函数作为基函数.基于样本的机器学习

要求学习机在容量控制和过拟合之间取一个折衷

从而保证学习机的推广能力和误差精度.本文通过在微分容量控制和最小化经验误差之间作一个折衷

提出基于微分容量控制的学习机.仿真实验验证了我们的学习机具有良好的推广能力.

Abstract

A differential method for capacity control is presented based on the analysis of the capacity of learning machines.Our method that can be applicable to the set of nonlinear hypothesis functions as well as the set of linear ones generalizes the theory about the capacity control of SVMs.A new learning machine is proposed based on differential capacity control method.In our learning machine

a good generalization performance can be obtained by the right balance struck between the empirical risk and the differential of the set of hypothesis functions that controls the machine capacity.Simulation results show the feasibility of our learning machine.

关键词

Keywords

references

Views

1099

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Anti-Noise Nonparallel Support Vector Machine with Margin Distribution

High Correct Recognition Rate Classifier Design with Appropriate Rejection Mechanism

A Survey on Visible-infrared Cross-modality Person Re-identification

A Theoretical Result on the Generalized Optimal Set of Statistically Uncorrelated Discriminant Vectors

A New Approach for Face Recognition Based on Singular Value Features and Neural Networks

Related Author

LIU Li-ming

LI Ping

CHU Mao-xiang

CAI Hong-bin

YANG Guo-wei

WAN Ming-hua

LAI Zhi-hui

ZHANG Fan-long

Related Institution

School of Electronic and Information Engineering， University of Science and Technology Liaoning

School of Electronic Information, Qingdao University

School of Information Engineering, Nanjing Audit University

School of Computer Science and Software Engineering, Shenzhen University

Hong Kong Polytechnic University, Kowloon

⁰