TY  - JOUR
A1  - Blanchard, Gilles
A1  - Mücke, Nicole
T1  - Kernel regression, minimax rates and effective dimensionality
BT  - beyond the regular case
JF  - Analysis and applications
N2  - We investigate if kernel regularization methods can achieve minimax convergence rates over a source condition regularity assumption for the target function. These questions have been considered in past literature, but only under specific assumptions about the decay, typically polynomial, of the spectrum of the the kernel mapping covariance operator. In the perspective of distribution-free results, we investigate this issue under much weaker assumption on the eigenvalue decay, allowing for more complex behavior that can reflect different structure of the data at different scales.
KW  - Kernel regression
KW  - minimax optimality
KW  - eigenvalue decay
Y1  - 2020
U6  - https://doi.org/10.1142/S0219530519500258
SN  - 0219-5305
SN  - 1793-6861
VL  - 18
IS  - 4
SP  - 683
EP  - 696
PB  - World Scientific
CY  - New Jersey
ER  -