The primary objective of this research is to obtain an accurate forecasting model for the US presidential election. To identify a reliable model, artificial neural networks (ANN) and support vector regression (SVR) models are compared based on some specified performance measures. Moreover, six independent variables such as GDP, unemployment rate, the president’s approval rate, and others are co...

Soil cation exchange capacity (CEC) is a parameter that represents soil fertility. Being difficult to measure, pedotransfer functions (PTFs) can be routinely applied for prediction of CEC by soil physicochemical properties that can be easily measured. This study developed the support vector regression (SVR) combined with genetic algorithm (GA) together with the adaptive network-based fuzzy infe...

Applications of neural machine interfaces have received increased attention during the last decades. It is crucial to realize the continuous control of prosthetic devices based on biological signals. In order to deal with the highly nonlinear relationship between the Electromyography (EMG) signals and motion, this study presents a novel decoding approach which employs multi-output support vecto...

Some algorithms in the primal have been recently proposed for training support vector machines. This letter follows those studies and develops a recursive finite Newton algorithm (IHLF-SVR-RFN) for training nonlinear support vector regression. The insensitive Huber loss function and the computation of the Newton step are discussed in detail. Comparisons with LIBSVM 2.82 show that the proposed a...

Time series prediction, especially financial time series prediction, is a challenging task in machine learning. In this issue, the data are usually non-stationary and volatile in nature. Because of its good generalization power, the support vector regression (SVR) has been widely applied in this application. The standard SVR employs a fixed -tube to tolerate noise and adopts the ‘p-norm (p 1⁄4 ...

Link adaptation in LTE-A is based on channel state information (CSI). For time-selective channels, CSI might be outdated already in the next subframe. Hence, CSI prediction must be employed. This paper investigates support vector regression (SVR) for channel extrapolation and prediction. SVR is applied for learning from the previous channel estimates in order to predict the CSI of the following...