.png){kind=icon}
返回
Robust truncated L2-norm twin support vector machine
.png){kind=icon}
PRE
AI
DOI：10.1007/s13042-021-01368-8.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 proposes a new robust truncated L-2-norm twin support vector machine ((TSVM)-S-2), where the truncated L-2-norm is used to measure the empirical risk to make the classifiers more robust when encountering lots of outliers. Meanwhile, chance constraints are also employed to specify false positive and false negative error rates. (TSVM)-S-2 considers a pair of chance constrained nonconvex nonsmooth problems. To solve these difficult problems, we propose an efficient iterative method for (TSVM)-S-2 based on difference of convex functions (DC) programs and DC Algorithms (DCA). Experiments on benchmark data sets and artificial data sets demonstrate the significant virtues of (TSVM)-S-2 in terms of robustness and generalization performance.
Keyword:
Twin support vector machine
Chance constraint
DC programming
DC Algorithm
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:
2.7
论文数:
3.2K
被引数:
5.6K
机构
引用论文
分享收藏Red Seaweed (Gracilaria verrucosa Greville) Based Polyurethane as Adsorptive Membrane for Ammonia Removal in Water.png)
Polymers
OAAI
PolymersIF0
OAAI分享收藏A METHOD OF COMPARING THE AREAS UNDER RECEIVER OPERATING CHARACTERISTIC CURVES DERIVED FROM THE SAME CASES一种比较相同情况下接收器工作特性曲线下面积的方法RADIOLOGY
RADIOLOGYIF15.2
PREAI
分享收藏分享收藏
查看更多内容