Sobolev Norm Learning Rates for Regularized Least-Squares Algorithm

@inproceedings{Fischer2017SobolevNL,
  title={Sobolev Norm Learning Rates for Regularized Least-Squares Algorithm},
  author={Simon Fischer and Ingo Steinwart},
  year={2017}
}
Learning rates for regularized least-squares algorithms are in most cases expressed with respect to the excess risk, or equivalently, the L2-norm. For some applications, however, guarantees with respect to stronger norms such as the L∞-norm, are desirable. We address this problem by establishing learning rates for a continuous scale of norms between the L2and the RKHS norm. As a byproduct we derive L∞-norm learning rates, and in the case of Sobolev RKHSs we actually obtain Sobolev norm learning… CONTINUE READING