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A chemometrician's guide to transfer learning.
- Source :
- Journal of Chemometrics; Nov2021, Vol. 35 Issue 11, p1-23, 23p
- Publication Year :
- 2021
-
Abstract
- Transfer learning (TL), the sub‐discipline of machine learning devoted to learning from different domains, has gained increasing attention over the past decade. With the current contribution, we aim at giving a concise overview on theory, concepts, and applications of TL from a chemometrician's perspective and draw some connections to previous work on calibration model updating/adaptation and calibration transfer. Furthermore, we provide a demonstration of the application of TL in analytical chemistry and discuss the benefits and challenges associate with its use for real‐world problems. We conclude the paper by discussing some open problems and by contemplating on future research directions. [ABSTRACT FROM AUTHOR]
- Subjects :
- ANALYTICAL chemistry
MACHINE learning
MULTISENSOR data fusion
Subjects
Details
- Language :
- English
- ISSN :
- 08869383
- Volume :
- 35
- Issue :
- 11
- Database :
- Complementary Index
- Journal :
- Journal of Chemometrics
- Publication Type :
- Academic Journal
- Accession number :
- 153674468
- Full Text :
- https://doi.org/10.1002/cem.3373