A Survey on Multi-Task Learning

Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to leverage useful information contained in multiple related tasks to help improve the generalization performance of all the tasks. In this paper, we give a survey for MTL from the perspective of algorithmic modeling...

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Vydané v:	IEEE transactions on knowledge and data engineering Ročník 34; číslo 12; s. 5586 - 5609
Hlavní autori:	Zhang, Yu, Yang, Qiang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.12.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms artificial intelligence Classification algorithms Clustering Cognitive tasks Computational modeling Data models Machine learning Modelling Multi-task learning Performance enhancement Supervised learning Task analysis Training Transfer learning
ISSN:	1041-4347, 1558-2191
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