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Algorithm-Dependent Generalization Bounds for Multi-Task Learning.

Published in IEEE transactions on pattern analysis and machine intelligence • Feb 1, 2017
NobleIDNI6P47W98R27S20
Authors:
Tongliang Liu
,
Dacheng Tao
,
Mingli Song

Abstract

Often, tasks are collected for multi-task learning (MTL) because they share similar feature structures. Based on this observation, in this paper, we present novel algorithm-dependent generalization bounds for MTL by exploiting the notion of algorithmic stability. We focus on the performance of one p...

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