.png){kind=icon}
返回
Twin neural network regression is a semi-supervised regression algorithm
.png){kind=icon}
OA
AI
DOI：10.1088/2632-2153/ac9885.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Twin neural network regression (TNNR) is trained to predict differences between the target values of two different data points rather than the targets themselves. By ensembling predicted differences between the targets of an unseen data point and all training data points, it is possible to obtain a very accurate prediction for the original regression problem. Since any loop of predicted differences should sum to zero, loops can be supplied to the training data, even if the data points themselves within loops are unlabelled. Semi-supervised training improves TNNR performance, which is already state of the art, significantly.
Keyword:
artificial neural networks
regression
semi-supervised learning
期刊
M
IF:
4.6
论文数:
1.1K
被引数:
3.4K
机构
引用论文
Real-time and offline techniques for identifying obstructive sleep apnea patients用于识别阻塞性睡眠呼吸暂停患者的实时和离线技术
.png){kind=icon}
PREAI
分享收藏分享收藏
分享收藏 没有更多内容
没有更多内容