Integral Operator Approach to Learning Theory with Unbounded Sampling

This paper mainly focuses on the least square regularized regression learning algorithm in a setting of unbounded sampling. Our task is to establish learning rates by means of integral operators. By imposing a moment hypothesis on the unbounded sampling outputs and a function space condition associated with marginal distribution ρ X , we derive learning rates which are consistent with those in the bounded sampling setting.