Kernel-based Information Criterion
Machine Learning
2014-12-16 v2
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).
Cite
@article{arxiv.1408.5810,
title = {Kernel-based Information Criterion},
author = {Somayeh Danafar and Kenji Fukumizu and Faustino Gomez},
journal= {arXiv preprint arXiv:1408.5810},
year = {2014}
}
Comments
We modified the reference 17, and the subcaptions of Figure 3