Flexible Clustered Multi-Task Learning by Learning Representative Tasks

Multi-task learning (MTL) methods have shown promising performance by learning multiple relevant tasks simultaneously, which exploits to share useful information across relevant tasks. Among various MTL methods, clustered multi-task learning (CMTL) assumes that all tasks can be clustered into groups...

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Vydané v:	IEEE transactions on pattern analysis and machine intelligence Ročník 38; číslo 2; s. 266 - 278
Hlavní autori:	Zhou, Qiang, Zhao, Qi
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.02.2016
Predmet:	Clustered Multi-Task Learning Covariance matrices Group Sparsity Kernel Learning systems Optimization Representative Task Robustness Training data Visualization representative task group sparsity Clustered multi-task learning
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
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