Generalization Bounds of Multitask Learning From Perspective of Vector-Valued Function Learning.

IEEE Transactions on Neural Networks and Learning Systems(2021)

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摘要
In this article, we study the generalization performance of multitask learning (MTL) by considering MTL as a learning process of vector-valued functions (VFs). We will answer two theoretical questions, given a small size training sample: 1) under what conditions does MTL perform better than single-task learning (STL)? And 2) under what conditions does MTL guarantee the consistency of all tasks dur...
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关键词
Task analysis,Complexity theory,Upper bound,Analytical models,Kernel,Probability,Learning systems
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