Linear regression (LR) and support vector regression (SVR) are widely used in data analysis. Geometrical correlation learning (GcLearn) was proposed recently to improve the predictive ability of LR and SVR through mining and using correlations between data of a variable (inner correlation). This paper theoretically analyzes prediction performance of the GcLearn method and proves that GcLearn LR...

OBJECTIVES Identifying the predictive factors of Sustained Virological Response (SVR) represents an important challenge in new interferon-based DAA therapies. Here, we analyzed the kinetics of antiviral response associated with a triple drug regimen, and the association between negative residual viral load at different time points during treatment. METHODS Twenty-three HCV genotype 1 (GT 1a n...

In this paper, we demonstrate the use of support vector regression (SVR) techniques for black-box system identification. These methods derive from statistical learning theory, and are of great theoretical and practical interest. We briefly describe the theory underpinning SVR, and compare support vector methods with other approaches using radial basis networks. Finally, we apply SVR to modeling...

In this paper, a new econometric model of volatility is proposed using hybrid Support Vector machine for Regression (SVR) combined with Chaotic Genetic Algorithm (CGA) to fit conditional mean and then conditional variance of stock market returns. The CGA, integrated by chaotic optimization algorithm with Genetic Algorithm (GA), is used to overcome premature local optimum in determining three hy...

Single nucleotide polymorphisms (SNPs) in the interleukin (IL)28B locus have been associated with a sustained virological response (SVR) in interferon-ribavirin (IFN-RBV)-treated chronic hepatitis C virus (HCV)-infected patients in European and African populations. In this study, the genotype frequency of two IL28B SNPs (rs129679860 and rs8099917) in a cohort of chronic HCV-monoinfected patient...

Abstract Accurate forecast of carbon dioxide (CO2) emissions plays a significant role in China's peaking and neutrality policies. A novel two-stage procedure based on support vector regression (SVR), random forest (RF), ridge (Ridge), artificial neural network (ANN) is proposed evaluated by comparing it with the single-stage procedure. Nine independent variables’ data (study period: 1985–2020) ...

This paper attempts to predict heavy metals (Pb, Zn and Cu) in the groundwater from Arak city, using support vector regression model(SVR) by taking major elements (HCO3, SO4) in the groundwater from Arak city. 150 data samples and several models were trained and tested using collected data to determine the optimum model in which each model involved two inputs and three outputs. This SVR model f...

This study developed a novel model, HGA-SVR, for type of kernel function and kernel parameter value optimization in support vector regression (SVR), which is then applied to forecast the maximum electrical daily load. A novel hybrid genetic algorithm (HGA) was adapted to search for the optimal type of kernel function and kernel parameter values of SVR to increase the accuracy of SVR. The propos...