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- W1498467740 abstract "Abstract : We study properties of algorithms which minimize (or almost-minimise) empirical error over a Donsker class of functions. we show that the L2-diameter of the set of almost-minimizers is converging to zero in probability. Therefore, as the number of samples grows, it is becoming unlikely that adding a point (or a number of points) to the training set will result in a large jump (in L2 distance) to a new hypothesis. We also show that under some conditions the expected errors of the almost-minimizers are becoming close with a rate faster than pi(exp -1/2)." @default.
- W1498467740 created "2016-06-24" @default.
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- W1498467740 date "2005-05-01" @default.
- W1498467740 modified "2023-10-18" @default.
- W1498467740 title "Some Properties of Empirical Risk Minimization Over Donsker Classes" @default.
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- W1498467740 doi "https://doi.org/10.21236/ada454986" @default.
- W1498467740 hasPublicationYear "2005" @default.
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