.png){kind=icon}
返回
Feature selection for support vector machines using Generalized Benders Decomposition
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ejor.2015.01.006.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We propose an exact method, based on Generalized Benders Decomposition, to select the best M features during induction. We provide details of the method and highlight some interesting parallels between the technique proposed here and some of those published in the literature. We also propose a relaxation of the problem where selecting too many features is penalized. The original method performs well on a variety of data sets. The relaxation, though competitive, is sensitive to the penalty parameter. (C) 2015 Elsevier B.V. All rights reserved.
Keyword:
Data mining
Feature selection
Support vector machines (SVM)
Generalized Benders Decomposition
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:
6
论文数:
2.2W
被引数:
6.4W
机构
暂无机构信息
引用论文
.png){kind=icon}
Physiological and behavioral responses to corticotropin-releasing factor administration: is CRF a mediator of anxiety or stress responses?促肾上腺皮质激素释放因子的生理和行为反应: CRF是焦虑或应激反应的中介物吗？
PREAI
分享收藏
查看更多内容