.png){kind=icon}
返回
Cutting Plane Training for Linear Support Vector Machines
.png){kind=icon}
PRE
AI
DOI：10.1109/TKDE.2011.247.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Support Vector Machines (SVMs) have been shown to achieve high performance on classification tasks across many domains, and a great deal of work has been dedicated to developing computationally efficient training algorithms for linear SVMs. One approach [1] approximately minimizes risk through use of cutting planes, and is improved by [2], [3]. We build upon this work, presenting a modification to the algorithm developed by Franc and Sonnenburg [2]. We demonstrate empirically that our changes can reduce cutting plane training time by up to 40 percent, and discuss how changes in data sets and parameter settings affect the effectiveness of our method.
Keyword:
Linear support vector machine
cutting plane SVM
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:
10.4
论文数:
6.9K
被引数:
3.2W
机构
引用论文
Purification and characterization of ciguatoxins from moray eel (Lycodontis javanicus, Muraenidae).png)
Toxicon
ToxiconIF0
PREAI
分享收藏
查看更多内容