  In this paper, we propose a novel {\em $e$-exponentiated} transformation, $0
\le e<1$, for loss functions. When the transformation is applied to a convex
loss function, the transformed loss function become more robust to outliers.
Using a novel generalization error bound, we have theoretically shown that the
transformed loss function has a tighter bound for datasets corrupted by
outliers. Our empirical observation shows that the accuracy obtained using the
transformed loss function can be significantly better than the same obtained
using the original loss function and comparable to that obtained by some other
state of the art methods in the presence of label noise.
