Statistical Science and Related Topics : Proceedings of Annual Workshop on Statistical Science and Related Topics, December 7, 2014, JOSAI UNIVERSITY

We focus on a selection of kernel parameters in the framework of the relevance vector machine (RVM) for regression, called the relevance vector regression (RVR). The RVR can achieve a sparse model and utilize a kernel function similar to the support vector regression (SVR). A crucial issue in the model building process of the RVR is the selection of the optimal values for kernel parameters. ln this paper, we derive a model selection criterion for evaluating the Bayesian predictive distribution of the RVR model from information-theoretic viewpoint. Monte Carlo experiments and real data analysis have been presented to demonstrate that the proposed modeling procedure performs well.Statistical Science and Related Topics : Prpceedings of Annual Workshop on Statistical Science and Reated Topics, held at Josai University on December 7 in 2014 / edited by Masatoshi IIDA, Manabu INUMA, Kiyoko NISHIZAWA, Takahiro TSUCHID