Bias corrected regularization kernel network and its applications
Regularization kernel network (RKN) is an effective and widely used kernel method for nonlinear regression analysis. In this paper, I characterize its bias and propose an approach to correct the bias. This leads to a new method called bias corrected regularization kernel network (BCRKN). Theoretical characterizations and simulation studies are used to verify the effectiveness of this bias corrected method. I show that BCRKN has smaller asymptotic bias but slightly larger variance. In applications of learning with a single data set, RKN and BCRKN have comparable performance if the regularization parameter is appropriately tuned. But in applications of incremental learning with block wise streaming data, BCKRN shows to be more efficient due to bias reduction.