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 se...

We introduce a generalized information criterion that contains other well-known criteria, such as Bayesian Criterion (BIC) and Akaike (AIC), special cases. Furthermore, the proposed spectral (SIC) is also more general than e.g., since knowledge of likelihood function not strictly required. SIC extracts geometric features error curve and, consequence, it can be considered an automatic elbow dete...