.png){kind=icon}
返回
An algorithm for training a large scale support vector machine for regression based on linear programming and decomposition methods
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patrec.2012.10.026.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper presents a method to train a Support Vector Regression (SVR) model for the large-scale case where the number of training samples supersedes the computational resources. The proposed scheme consists of posing the SVR problem entirely as a Linear Programming (LP) problem and on the development of a sequential optimization method based on variables decomposition, constraints decomposition, and the use of primal-dual interior point methods. Experimental results demonstrate that the proposed approach has comparable performance with other SV-based classifiers. Particularly, experiments demonstrate that as the problem size increases, the sparser the solution becomes, and more computational efficiency can be gained in comparison with other methods. This demonstrates that the proposed learning scheme and the LP-SVR model are robust and efficient when compared with other methodologies for large-scale problems. (C) 2012 Elsevier B.V. All rights reserved.
Keyword:
Support vector machines
Support Vector Regression
Linear programming
Interior point methods
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.3
论文数:
7.9K
被引数:
1.6W
机构
引用论文
On the Development and Applications of Cellulosic Nanofibrillar and Nanocrystalline Materials纤维素纳米原纤和纳米晶材料的发展与应用
.png){kind=icon}
PREAI
分享收藏The FERET database and evaluation procedure for face-recognition algorithms人脸识别算法的FERET数据库和评估程序
PREAI
分享收藏
查看更多内容