Multitask Learning
Multitask Learning
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DOI：
10.1007/978-1-4615-5529-2_5
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发表时间：
1998-01-01
期刊：
影响因子：
--
通讯作者：
Caruana, R
中科院分区：
文献类型：
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作者：
Caruana, R
多任务学习是一种归纳迁移方法，它利用相关任务训练信号中包含的域信息作为归纳偏差来提高泛化能力。它通过使用共享表示并行学习任务来实现这一点；从每个任务中学到的东西可以帮助你更好地学习其他任务。本文回顾了前人关于MTL的研究成果，提出了新的证据，证明反向网络中的MTL在不需要监督信号的情况下发现了任务相关性，并给出了基于k近邻和核回归的MTL的新结果。在本文中，我们在三个领域展示了多任务学习。我们解释了多任务学习是如何工作的，并表明在现实领域中有很多多任务学习的机会。提出了一种基于案例的k近邻和核回归方法的多任务学习算法和结果，并概述了一种基于决策树的多任务学习算法。由于多任务学习有效，可以应用于许多不同类型的领域，并且可以与不同的学习算法一起使用，我们推测将有很多机会将其用于现实世界的问题。
Multitask Learning is an approach to inductive transfer that improves generalization by using the domain information contained in the training signals of related tasks as an inductive bias. It does this by learning tasks in parallel while using a shared representation; what is learned for each task can help other tasks be learned better. This paper reviews prior work on MTL, presents new evidence that MTL in backprop nets discovers task relatedness without the need of supervisory signals, and presents new results for MTL with k-nearest neighbor and kernel regression. In this paper we demonstrate multitask learning in three domains. We explain how multitask learning works, and show that there are many opportunities for multitask learning in real domains. We present an algorithm and results for multitask learning with case-based methods like k-nearest neighbor and kernel regression, and sketch an algorithm for multitask learning in decision trees. Because multitask learning works, can be applied to many different kinds of domains, and can be used with different learning algorithms, we conjecture there will be many opportunities for its use on real-world problems.