Transfer bounds for linear feature learning

被引：0
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作者：
Andreas Maurer
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来源：
Machine Learning | 2009年 / 75卷
关键词：
Learning to learn; Transfer learning; Kernel methods; Generalization;
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暂无
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学科分类号：
摘要：
If regression tasks are sampled from a distribution, then the expected error for a future task can be estimated by the average empirical errors on the data of a finite sample of tasks, uniformly over a class of regularizing or pre-processing transformations. The bound is dimension free, justifies optimization of the pre-processing feature-map and explains the circumstances under which learning-to-learn is preferable to single task learning.
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页码：327 / 350
页数：23
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