Transfer learning in a heterogeneous environment
Transfer learning in a heterogeneous environment
复制标题
异构环境中的迁移学习
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
M. Pontil
中科院分区：
文献类型：
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作者：
Andreas Maurer;M. Pontil
我们提出了一种迁移学习的方法，在该方法中，过去遇到的任务被用来选择一个表示，预计将在未来的任务。每个任务被假设为希尔伯特空间中的二进制分类或回归。我们建议将观察到的任务分组，并分配一个低维投影到每个组。选择组和相应的投影以最小化经验误差标准。为了学习未来的任务，人们选择投影和相应的线性函数，经验误差最小。当应用到未来的任务时，这种方法的预期误差被证明是一致的经验误差准则的范围。该界与Hilbert空间的维数无关。讨论了迁移学习相对于单一任务学习的优势以及任务分组相对于不分组的优势。
We present a method for transfer learning, in which tasks encountered in the past are used to choose a representation which is expected to work well on future tasks. Each task is assumed to be binary classification or regression in a Hilbert space. We propose to arrange the observed tasks into groups and to assign a low-dimensional projection to each group. The groups and the corresponding projections are chosen to minimize an empirical error criterion. To learn a future task, one selects the projection, and the corresponding linear function, for which the empirical error is minimal. The expected error of this method when applied to a future task is shown to be uniformly bounded by the empirical error criterion. The bound is independent of the dimension of the Hilbert space. The advantages of transfer learning over single task learning and the advantages of task grouping over no grouping are discussed.
影响因子：
7.5
作者：
Argyriou, Andreas;Evgeniou, Theodoros;Pontil, Massimiliano
通讯作者：
Pontil, Massimiliano