.png){kind=icon}
返回
Learning using privileged information: SVM plus and weighted SVM
.png){kind=icon}
OA
AI
DOI：10.1016/j.neunet.2014.02.002.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Prior knowledge can be used to improve predictive performance of learning algorithms or reduce the amount of data required for training. The same goal is pursued within the learning using privileged information paradigm which was recently introduced by Vapnik et al. and is aimed at utilizing additional information available only at training time a framework implemented by SVM+. We relate the privileged information to importance weighting and show that the prior knowledge expressible with privileged features can also be encoded by weights associated with every training example. We show that a weighted SVM can always replicate an SVM+ solution, while the converse is not true and we construct a counterexample highlighting the limitations of SVM+. Finally, we touch on the problem of choosing weights for weighted SVMs when privileged features are not available. (C) 2014 Elsevier Ltd. All rights reserved.
Keyword:
SVM
SVM
Weighted SVM
Importance weighting
Privileged information
Prior knowledge
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:
6.3
论文数:
8.3K
被引数:
3.0W
机构
引用论文
The reliability investigation of gold plated electrical contacts under sliding conditions.png)
Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing)
Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing)IF0
PREAI
分享收藏分享收藏
查看更多内容