Gaussian process model based multi-source labeled data transfer learning for reducing cost of modeling target chemical processes with unlabeled data-Reference-Cited by-同舟云学术

Gaussian process model based multi-source labeled data transfer learning for reducing cost of modeling target chemical processes with unlabeled data

Published:2021-12 Issue: Volume:117 Page:104941
ISSN:0967-0661
Container-title:Control Engineering Practice
language:en
Short-container-title:Control Engineering Practice

Author:

Chan Lester Lik Teck^ORCID,Chen Junghui

Funder

Ministry of Science and Technology of the People's Republic of China

Ministry of Science and Technology, Taiwan

Publisher

Elsevier BV

Subject

Applied Mathematics,Electrical and Electronic Engineering,Computer Science Applications,Control and Systems Engineering

Reference34 articles.

1. Chan, L. L. T., & Chen, J. (2019). Reducing cost of process modeling through multi-source data transfer learning. In 2019 12th asian control conference (pp. 474–479).

2. Semi-supervised learning;Chapelle,2010

3. Multisource domain adaptation and its application to early detection of fatigue;Chattopadhyay;ACM Transactions on Knowledge Discovery from Data,2012

4. Transfer learning based fault diagnosis with missing data due to multi-rate sampling;Chen;Sensors,2019

5. Locally weighted regression: An approach to regression analysis by local fitting;Cleveland;Journal of the American Statistical Association,1988

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