Sharper lower bounds on the performance of the empirical risk minimization algorithm
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Description
We present an argument based on the multidimensional and the uniform central limit theorems, proving that, under some geometrical assumptions between the target function T and the learning class F, the excess risk of the empirical risk minimization algorithm is lower bounded by Esup q∈Q Gq/δ,/n where (Gq)q∈Q is a canonical Gaussian process associated with Q (a well chosen subset of F) and δ is a parameter governing the oscillations of the empirical excess risk function over a small ball in F.
Collections | ANU Research Publications |
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Date published: | 2010 |
Type: | Journal article |
URI: | http://hdl.handle.net/1885/55533 |
Source: | Bernoulli |
DOI: | 10.3150/09-BEJ225 |
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01_Lecue_Sharper_lower_bounds_on_the_2010.pdf | 112.87 kB | Adobe PDF |
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