  This paper introduces Kernel-based Information Criterion (KIC) for model
selection in regression analysis. The novel kernel-based complexity measure in
KIC efficiently computes the interdependency between parameters of the model
using a variable-wise variance and yields selection of better, more robust
regressors. Experimental results show superior performance on both simulated
and real data sets compared to Leave-One-Out Cross-Validation (LOOCV),
kernel-based Information Complexity (ICOMP), and maximum log of marginal
likelihood in Gaussian Process Regression (GPR).
