Ensuring the Robustness and Reliability of Data-Driven Knowledge Discovery Models in Production and Manufacturing.

导出 LinkOut

翻译打印收藏纠错

第一作者： Shailesh,Tripathi

第一单位： Production and Operations Management, University of Applied Sciences Upper Austria, Linz, Austria.

作者： Shailesh,Tripathi ^[1] ; David,Muhr ^[1] ; Manuel,Brunner ^[1] ; Herbert,Jodlbauer ^[1] ; Matthias,Dehmer ^[2] ; Frank,Emmert-Streib ^[3]

作者单位： Production and Operations Management, University of Applied Sciences Upper Austria, Linz, Austria. ^[1] Department of Computer Science, Swiss Distance University of Applied Sciences, Brig, Switzerland. ^[2] School of Science, Xian Technological University, Xian, China. ^[3] Department of Biomedical Computer Science and Mechatronics, UMIT-The Health and Life Science University, Hall in Tyrol, Austria. ^[4] College of Artificial Intelligence, Nankai University, Tianjin, China. ^[5] Predictive Society and Data Analytics Lab, Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland. ^[6] Institute of Biosciences and Medical Technology, Tampere University, Tampere, Finland. ^[7]

关键词 CRISP- DM industrial production industry 4.0 machine learning robustness smart manufacturing

DOI 10.3389/frai.2021.576892

PMID 34195608

发布时间 2024-04-02

翻译满意度评价：

准确一般字义偏差语意偏离译文作用小提交

浏览0

Frontiers in artificial intelligence

2021年4卷

576892页

相似文献

中文期刊
外文期刊
学位论文
会议论文

检索历史清除

Ensuring the Robustness and Reliability of Data-Driven Knowledge Discovery Models in Production and Manufacturing.

Frontiers in artificial intelligence

相似文献

Frontiers in artificial intelligence

相关期刊

参考文献：
指定格式：	NoteExpress EndNote RefWorks NoteFirst

检索历史 清除

Ensuring the Robustness and Reliability of Data-Driven Knowledge Discovery Models in Production and Manufacturing.

Frontiers in artificial intelligence

相似文献

Frontiers in artificial intelligence

相关期刊

检索历史清除