Abstract

This paper introduces Kernel-based Information Criterion (KIC) for model selection in regression analysis. The kernel-based complexity measure in KIC efficiently computes the interdependency between parameters of the model using a novel 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).

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