Local Rademacher Complexity Bounds based on Covering Numbers
October 06, 2015 Β· Declared Dead Β· π Neurocomputing
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Authors
Yunwen Lei, Lixin Ding, Yingzhou Bi
arXiv ID
1510.01463
Category
cs.AI: Artificial Intelligence
Cross-listed
cs.LG,
stat.ML
Citations
23
Venue
Neurocomputing
Last Checked
4 months ago
Abstract
This paper provides a general result on controlling local Rademacher complexities, which captures in an elegant form to relate the complexities with constraint on the expected norm to the corresponding ones with constraint on the empirical norm. This result is convenient to apply in real applications and could yield refined local Rademacher complexity bounds for function classes satisfying general entropy conditions. We demonstrate the power of our complexity bounds by applying them to derive effective generalization error bounds.
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