Tight bounds for the expected risk of linear classifiers and PAC-bayes finite-sample guarantees
We analyze the expected risk of linear classifiers for a fixed weight vector in the “minimax” setting. That is, we analyze the worst-case risk among all data distributions with a given mean and covariance. We provide a simpler proof of the tight polynomial-tail bound for general random variables. Fo...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Journal of Machine Learning Research
2018
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| Online Access: | http://hdl.handle.net/1721.1/113045 https://orcid.org/0000-0003-0238-6384 https://orcid.org/0000-0002-2199-0379 |