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arXiv · 1408.5810

Kernel-based Information Criterion

Somayeh Danafar·Kenji Fukumizu·Faustino Gomez
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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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Somayeh Danafar, Kenji Fukumizu, Faustino Gomez. 2014-08-25. Kernel-based Information Criterion. https://arxiv.org/abs/1408.5810

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