Applying the learning bound to kernel regression

This lecture applies the main theorem of the course to the case of least square regression in a Reproducing Kernel Hilbert Space (RKHS), discussing the application to LR of the Rademacher bound and the Lipschitz constant of the mapping. The slides cover various aspects of the application, including the family of functions, probability considerations, and the risk associated with the regression. The lecture delves into the complexity of the functions, the interval of values, and the rate of convergence, emphasizing the key concepts of the learning bound and kernel regression.