− Instead of minimizing the observed training error, Support Vector Regression (SVR) attempts to minimize the generalization error bound so as to achieve generalized performance. The idea of SVR is based on the computation of a linear regression function in a high dimensional feature space where the input data are mapped via a nonlinear function. SVR has been applied in various fields – time se...

This paper presents an analysis of several different LSI (latent semantic indexing) query approaches and proposes a novel rescaling technique, namely singular value rescaling (SVR). Experiments on a standardized TREC data set confirmed the effectiveness of SVR, showing an improvement ratio of 5.9% over the best conventional LSI query approach. In addition, we also compared SVR with another scal...

This paper demonstrates different types support vector regression (SVR) for annealing robust fuzzy neural networks (ARFNNs) to identification of nonlinear magneto-rheological (MR) damper with outliers. A SVR has the good performances to determine the number of rule in the simplified fuzzy inference system and initial weights for the fuzzy neural networks. In this paper, we independently propose...

A reinforcement learning algorithm is proposed to improve the accuracy of short-term load forecasting (STLF) in this article. The proposed model integrates radial basis function neural network (RBFNN), support vector regression (SVR), and adaptive annealing learning algorithm (AALA). In the proposed methodology, firstly, the initial structure of RBFNN is determined by using an SVR. Then, an AAL...

A number of machine learning (ML) techniques have recently been proposed to solve color constancy problem in computer vision. Neural networks (NNs) and support vector regression (SVR) in particular, have been shown to outperform many traditional color constancy algorithms. However, neither neural networks nor SVR were compared to simpler regression tools in those studies. In this article, we pr...

This paper presents an efficient currency option pricing model based on support vector regression (SVR). This model focuses on selection of input variables of SVR. We apply stochastic volatility model with jumps to SVR in order to account for sudden big changes in exchange rate volatility. We use forward exchange rate as the input variable of SVR, since forward exchange rate takes interest rate...

Sustained virologic response (SVR) is defined as aviremia 24 weeks after completion of antiviral therapy for chronic hepatitis C virus (HCV) infection. In analyses of SVR durability, the incidence of late relapse is extremely low (<1%). Histologic regression of both necroinflammation and fibrosis has been demonstrated in paired liver biopsy samples in SVR-achieving patients. More noteworthy is ...

BACKGROUND/AIMS Low gamma-glutamyltransferase (GGT) level was shown to be an independent predictor of a sustained virological response (SVR) in chronic hepatitis C. We aimed to determine factors associated with high GGT level, and to evaluate whether low GGT level is an independent predictor of a SVR in chronic hepatitis C genotype 1. METHODS We retrospectively reviewed our data of patients w...

XIE, Y. G., H. Y. ZHANG, H. Y. WANG, L. F. WANG and ZH. M. YUAN, 2013. Prediction of multidimensional time series based on GS-RSR-SVR and its application in agricultural economy. Bulg. J. Agric. Sci., 19: 1327-1336 This paper proposes a method that creatively applies a Geo-statistics tool (GS) to complete fast and adequate order determination and introduces a novel algorithm, named Reasonable S...

In this paper, Support Vector Regression (SVR) training models using three different kernels: polynomial, Radial Basis Function (RBF), and mixed kernels, are constructed to demonstrate the training performance of unarranged data obtained from 32 virtual 3-D computer models. The 32 samples used as input data for training the three SVR models are represented by the coordination value sets of poin...