Twin support vector regression (TSVR), as an effective regression machine, solves a pair of smaller-sized quadratic programming problems (QPPs) rather than a single large one as in the classical support vector regression (SVR), which makes the learning speed of TSVR approximately 4 times faster than that of the SVR. However, the empirical risk minimization principle is implemented in TSVR, whic...

Motivated by nonparallel hyperplanes support vector machine (NHSVM), a new regression method of data, named as nonparallel hyperplanes support vector regression (NHSVR), is proposed in this paper. The advantages of NHSVR have two aspects, one is considering the minimization of structure risk by introducing a regularization term in objective function, and another is finding two nonparallel hyper...

The main purpose of twin support vector regression (TSVR) is to find linear or nonlinear relationships in sample data, and then predict future data. TSVR the decomposition a large convex quadratic programming problem into two small problems. Therefore, not only has advantages fast computation low computational complexity, but also better performance. Classic SVR, assuming that accords with mean...

Twin support vector regression (TSVR) is a new regression algorithm, which aims at finding -insensitive upand down-bound functions for the training points. In order to do so, one needs to resolve a pair of smaller-sized quadratic programming problems (QPPs) rather than a single large one in a classical SVR. However, the same penalties are given to the samples in TSVR. In fact, samples in the di...

The present study aims at developing a forecasting model to predict the next year’s air pollution concentrations in the atmosphere of Iran. In this regard, it proposes the use of ARIMA, SVR, and TSVR, as well as hybrid ARIMA-SVR and ARIMA-TSVR models, which combined the autoregressive part of the autoregressive integrated moving average (ARIMA) model with the support vector regression technique...

The present study aims at developing a forecasting model to predict the next year’s air pollution concentrations in the atmosphere of Iran. In this regard, it proposes the use of ARIMA, SVR, and TSVR, as well as hybrid ARIMA-SVR and ARIMA-TSVR models, which combined the autoregressive part of the autoregressive integrated moving average (ARIMA) model with the support vector regression technique...

In this paper, a new unconstrained convex minimization problem formulation is proposed as the Lagrangian dual of the 2-norm twin support vector regression (TSVR). The proposed formulation leads to two smaller sized unconstrained minimization problems having their objective functions piece-wise quadratic and differentiable. It is further proposed to apply gradient based iterative method for solv...

Twin support vector machine (TWSVM) and twin regression (TSVR) are newly emerging efficient learning techniques which offer promising solutions for classification challenges respectively. TWSVM is based upon the idea to identify two nonparallel hyperplanes classify data points their respective classes. It requires solve small sized quadratic programming problems (QPPs) in lieu of solving single...

—The research presents -hierarchical fuzzy twin support vector regression (-HFTSVR) based on -fuzzy twin support vector regression (-FTSVR) and -twin support vector regression (-TSVR). -FTSVR is achieved by incorporating trapezoidal fuzzy numbers to -TSVR which takes care of uncertainty existing in forecasting problems. -FTSVR determines a pair of -insensitive proximal functions by so...