RVM Kernel Parameters Selection Method based on AIC Applied in Gold Prices

Abstract

The relationship between the nuclear parameters and model performance is complex, which is from relevance vector machine (RVM) regression model based on Gaussian radial basis kernel function. Aiming at the problem of how to determine the kernel parameters of RVM, a method to selecting kernel parameter of RVM based on AIC criterion is proposed. Firstly, a novel of statistic Q is proposed based on “Akaike” Information Criterion (AIC), while the Q is as a fitness function. Secondly, we use the differential evolution algorithm (Differential Evolution Algorithm, DE) to find the best kernel parameter, in order to choose determine the kernel parameters. Finally, a RVM regression model mode is established and it is used in predicting gold price. Experimental results show that the prediction model has higher precision and better fitting the generalization ability than the traditional method, which demonstrates the AIC-based criteria for selecting RVM kernel parameter method is effective and feasible.