nonparametric approach based on a combination of kernel logistic regression and ¡support vector regression. …

Recurrent Support Vector Regression for a Nonlinear ARMA Model with Applications to Forecasting Financial Returns … Abstract: Motivated by the recurrent Neural Networks, this paper proposes a recurrent Support Vector Regression (SVR) procedure … opposed to the usual feedforward SVR. -- Recurrent support vector regression ; MLE ; recurrent MLP ; nonlinear ARMA …

predicting techniques for fatigue life of composite materials are not widely investigated. The support vector regression (SVR) is … feasibility of developed SVRIA (support vector regression with immune alforithms) approach in predicting fatigue life of composite …

This study attempts to assess the forecasting accuracy of Support Vector Regression (SVR) with regard to other …

Motivated by recurrent neural networks, this paper proposes a recurrent support vector regression (SVR) procedure to …

essential. Support vector regression (SVR) applies the structural risk minimization principle to minimize an upper bound of the … methods, namely SVMG, regression model, and ANN model …

Traditional time series forecasting models are difficult to capture the nonlinear patterns. Support vector regression … (SVR) has been successfully used to solve nonlinear regression and times series problems. However, parameters determination …

. However, RPU in the car-rental industry suffers serious nonlinearity. Recently, support vector regression (SVR) has been … successfully applied to solve nonlinear regression and time series problems. This study elucidates the feasibility of using SVR to … DTAG (Dollar Thrifty Automotive Group, USA) are used to illustrate the proposed SVRIA (support vector regression with …

Traditional time series forecasting models are difficult to capture the nonlinear patterns. Support vector regression … (SVR) has been successfully used to solve nonlinear regression and times series problems. However, parameters determination …