Kernel-based Information Criterion

2014/08/25 by Somayeh Danafar, Kenji Fukumizu, Danafar, Somayeh +3
Computer Science · Engineering · Mathematics · #FOS: Computer and information sciences #Fault Detection and Control Systems #Gaussian Processes and Bayesian Inference #Machine Learning (stat.ML) #Neural Networks and Applications #stat.ML

paper · pdf · doi:10.48550/arxiv.1408.5810

We modified the reference 17, and the subcaptions of Figure 3

openalex publication_date 2014/08/25 · arxiv created 2014/12/15 · arxiv updated 2014/12/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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).

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