of SVR (support vector regression) technique. Several statistical criterions, (i.e. MAPE (mean absolute percentage error …

model, the polynomial and radial basis function (RBF) are applied as the kernel function of Support Vector Regression (SVR …

-known ν-tube Support Vector Regression (ν-SVR) is an effective method for implementing this idea in the context of covariates …

used least squares support vector regression (LSSVR) for prediction. An optimized radial basis function was obtained as …

Kernel-based methods, such as support vector regression (SVR), have demonstrated satisfactory performance in short …

-family residential sector. In this paper, we build a sensor-based forecasting model using Support Vector Regression (SVR), a commonly …

of principal component analysis, support vector regression and weather forecast data. One-day ahead regional forecasts of …

(CSVQR) that combines the formulations of support vector regression and composite quantile regression. First the CSVQR using …

noise in support vectors may lead to an over-fitting and in turn to a poor generalization. The standard support vector … regression (SVR) is reformulated in this article in such a way that the large errors which correspond to noise are restricted by …

based on Particle Swarm Optimization (PSO-ANN), Support Vector Regression (SVR), Multi- Layer Artificial Neural Networks …