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Lower Bounds for the Empirical Minimization Algorithm

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1 Author(s)
Mendelson, S. ; Centre for Math. & its Applic., Australian Nat. Univ., Canberra, ACT

In this correspondence, we present a simple argument that proves that under mild geometric assumptions on the class F and the set of target functions T, the empirical minimization algorithm cannot yield a uniform error rate that is faster than 1/radic(k) in the function learning setup. This result holds for various loss functionals and the target functions from T that cause the slow uniform error rate are clearly exhibited.

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Information Theory, IEEE Transactions on  (Volume:54 ,  Issue: 8 )