The Implementation of Least Squares Hidden Space Support Vector Machines Based on Improved Conjugate Gradient Algorithm

WANG Wei; LOU Li

Chinese

您当前的位置：

首页 >

文章列表页 >

The Implementation of Least Squares Hidden Space Support Vector Machines Based on Improved Conjugate Gradient Algorithm

更新时间：2026-09-04

- The Implementation of Least Squares Hidden Space Support Vector Machines Based on Improved Conjugate Gradient Algorithm
- Vol. 29, Issue 12, Pages: 99-102(2012)
- 作者机构：
  
  西安石油大学机构计算机学院,陕西,西安,710065
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2012
- 稿件说明：
移动端阅览
王玮, 娄莉. 基于改进的共轭梯度算法实现的最小二乘隐空间支持向量机[J]. 微电子学与计算机, 2012, 29(12): 99-102.

WANG Wei, LOU Li. The Implementation of Least Squares Hidden Space Support Vector Machines Based on Improved Conjugate Gradient Algorithm[J]. 2012, 29(12): 99-102.
王玮, 娄莉. 基于改进的共轭梯度算法实现的最小二乘隐空间支持向量机[J]. 微电子学与计算机, 2012, 29(12): 99-102. DOI：

WANG Wei, LOU Li. The Implementation of Least Squares Hidden Space Support Vector Machines Based on Improved Conjugate Gradient Algorithm[J]. 2012, 29(12): 99-102. DOI：

摘要

本文研究了最小二乘隐空间支持向量机的优化问题.文中采用基于对称超松弛预处理技术改进共轭梯度算法

改进的共轭梯度算法只需求解一个阶数为l-1的线性代数方程组即可

大大节省了计算时间.最后将其应用于最小二乘隐空间支持向量机中建立数学模型

并通过实例验证了该算法的优越性.

Abstract

This research main focused on the improvement on Least Squares Hidden Space Support Vector Machines. Using Symmetric Successive Over-Relaxation technology to improve Conjugate Gradient Algorithm

it saves a lot of execution time costs for solving the system of linear algebraic equations with L-1 orders. At the end of this paper

the advantage of this improved algorithm is proved by applying it on mathematics modeling.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

A review of large model development: technological evolution from BERT to Deepseek

Microservices anomaly detection based on the correlation of monitoring metrics

A feature-oriented context understanding and slice analysis network for tuberculosis detection

A high-efficiency charge pump for ultra-low voltage boost systems

Steganography model guided by attention mechanism and improved dense convolution

Related Author

No data

Related Institution

No data

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰