  Learning with Fredholm kernel has attracted increasing attention recently
since it can effectively utilize the data information to improve the prediction
performance. Despite rapid progress on theoretical and experimental
evaluations, its generalization analysis has not been explored in learning
theory literature. In this paper, we establish the generalization bound of
least square regularized regression with Fredholm kernel, which implies that
the fast learning rate O(l^{-1}) can be reached under mild capacity conditions.
Simulated examples show that this Fredholm regression algorithm can achieve the
satisfactory prediction performance.
