On the Consistency of the Bootstrap Approach for Support Vector Machines and Related Kernel Based Methods
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 for approximating the unknown finite sample distribution of SVMs by the bootstrap approach.