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- W2753709519 abstract "Abstract As a promising area in machine learning, multi-task learning (MTL) aims to improve the performance of multiple related learning tasks by leveraging useful information among them. In this paper, we give an overview of MTL by first giving a definition of MTL. Then several different settings of MTL are introduced, including multi-task supervised learning, multi-task unsupervised learning, multi-task semi-supervised learning, multi-task active learning, multi-task reinforcement learning, multi-task online learning and multi-task multi-view learning. For each setting, representative MTL models are presented. In order to speed up the learning process, parallel and distributed MTL models are introduced. Many areas, including computer vision, bioinformatics, health informatics, speech, natural language processing, web applications and ubiquitous computing, use MTL to improve the performance of the applications involved and some representative works are reviewed. Finally, recent theoretical analyses for MTL are presented." @default.
- W2753709519 created "2017-09-15" @default.
- W2753709519 creator A5018143257 @default.
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- W2753709519 date "2017-09-01" @default.
- W2753709519 modified "2023-10-11" @default.
- W2753709519 title "An overview of multi-task learning" @default.
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- W2753709519 doi "https://doi.org/10.1093/nsr/nwx105" @default.
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