In the recent years, the use of GARCH type (especially, ARMA-GARCH) models and computational-intelligence-based techniques—Support Vector Machine (SVM) and Relevance Vector Machine (RVM) have been successfully used for financial forecasting. This paper deals with the application of ARMA-GARCH, recurrent SVM (RSVM) and recurrent RVM (RRVM) in volatility forecasting. Based on RSVM and RRVM, two G...

In the oil layer recognition, Relevance vector machines (RVM) have a good effect. But the single kernel function RVM has some limitations, a kind of multi-kernel function RVM based on particle swarm optimization (PSO) is proposed, which includes the model parameter estimation, model optimization on multi-kernel function RVM, PSO-based training, and recognition. The results of simulation experim...

The task of RBF kernel selection in Relevance Vector Machines (RVM) is considered. RVM exploits a probabilistic Bayesian learning framework offering number of advantages to state-of-the-art Support Vector Machines. In particular RVM effectively avoids determination of regularization coefficient C via evidence maximization. In the paper we show that RBF kernel selection in Bayesian framework req...

Relevance vector machines (RVM) have recently attracted much interest in the research community because they provide a number of advantages. They are based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. As a consequence, they can generalize well and provide inferences at low computational cost. In this tutorial we first present the...