On generalization bounds, projection profile, and
and Dan Roth.
We study generalization properties of linear learning
algorithms and develop a data dependent approach that is
used to derive generalization bounds that depend on the
margin distribution. Our method makes use of random
projection techniques to allow the use of existing VC
dimension bounds in the effective, lower, dimension of
the data. Comparisons with existing generalization
bound show that our bounds are tighter and meaningful in
cases existing bounds are not.