Convergence analysis of online algorithms for vector-valued kernel regression

arXiv:2309.07779v2 Announce Type: replace
Abstract: We consider the problem of approximating the regression function from noisy vector-valued data by an online learning algorithm using an appropriate reproducing kernel Hilbert space (RKHS) as prior. In an online algorithm, i.i.d. samples become available one by one by a random process and are successively processed to build approximations to the regression function. We are interested in the asymptotic performance of such online approximation algorithms and show that the expected squared error in the …

abstract algorithm algorithms analysis arxiv become convergence cs.na data function kernel math.na online learning prior process random regression samples space stat.ml type vector