TY - GEN
T1 - Online Learning in Reproducing Kernel Hilbert Space With Non-IID Data
AU - Zhang, Xiwei
AU - Li, Tao
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - We analyze the convergence of online regularized learning algorithm based on dependent and non-stationary online data streams for the nonparametric regression problem in reproducing kernel Hilbert space (RKHS). We show that the algorithm achieves mean-square convergence if the algorithm gain and regularization parameter are chosen appropriately, the online data streams are weakly dependent and satisfy the eigenvalue-wise persistence of excitation condition. Especially, for the case with independent but non-identically distributed online data streams, we give more intuitive convergence conditions on the drifts of the probability measures induced by the data.
AB - We analyze the convergence of online regularized learning algorithm based on dependent and non-stationary online data streams for the nonparametric regression problem in reproducing kernel Hilbert space (RKHS). We show that the algorithm achieves mean-square convergence if the algorithm gain and regularization parameter are chosen appropriately, the online data streams are weakly dependent and satisfy the eigenvalue-wise persistence of excitation condition. Especially, for the case with independent but non-identically distributed online data streams, we give more intuitive convergence conditions on the drifts of the probability measures induced by the data.
UR - https://www.scopus.com/pages/publications/85184811507
U2 - 10.1109/CDC49753.2023.10384155
DO - 10.1109/CDC49753.2023.10384155
M3 - 会议稿件
AN - SCOPUS:85184811507
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 6610
EP - 6615
BT - 2023 62nd IEEE Conference on Decision and Control, CDC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 62nd IEEE Conference on Decision and Control, CDC 2023
Y2 - 13 December 2023 through 15 December 2023
ER -