An overview of multi-task learning  

作　　者：yu zhang qiang yang Yu Zhang;QiangYang(Department of Computer Science and Engineering, Hong Kong University of Science arid Technology, Hong Kong, Chin)

机构地区：[1]department of computer science and engineering,hong kong university of science and technology,Hong Kong,China

出　　处：《National Science Review》2018年第1期30-43,共14页国家科学评论（英文版）

基　　金：supported by the National Basic Research Program of China (973 Program) (2014CB340304);the Hong Kong CERG projects(16211214, 16209715 and 16244616);the National Natural Science Foundation of China (61473087 and 61673202);the Natural Science Foundation of Jiangsu Province(BK20141340)

摘　　要：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, bio info rmatics, 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.As a promising area in machine learning, multi-tasklearning （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.

关 键 词：multi-task learning 

分 类 号：N0[自然科学总论—科学技术哲学]