Convergence Analysis of Online Algorithms for Vector-Valued Kernel Regression

The core message of this article is to provide a sharp asymptotic estimate for the expected squared error of online learning algorithms that approximate the regression function from noisy vector-valued data using a reproducing kernel Hilbert space (RKHS) as a prior.