Andreas Christmann, Robert Hable
Published 2013-01-29Version 1
It is shown that bootstrap approximations of support vector machines (SVMs) based on a general convex and smooth loss function and on a general kernel are consistent. This result is useful to approximate the unknown finite sample distribution of SVMs by the bootstrap approach.
A note on the consistency of the random forest algorithm
Qualitative Robustness of Support Vector Machines
Feature Elimination in Kernel Machines in moderately high dimensions
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