Twin support vector machine (TWSVM) and twin support vector regression (TSVR) are newly
emerging efficient machine learning techniques which offer promising solutions for …

The sensitivity of tunnel boring machines (TBMs) to complex rock mass parameters makes
the accurate and reliable prediction of these parameters crucial for the selection of …

Fuzzy time series forecasting (FTSF) methods avoid the basic assumptions of traditional time
series forecasting (TSF) methods. The FTSF methods consist of four stages namely …

This study proposes a new regressor—ε-twin support vector regression (ε-TSVR) based on
TSVR. ε-TSVR determines a pair of ε-insensitive proximal functions by solving two related …

The main objective of twin support vector regression (TSVR) is to find the optimum
regression function based on the ε-insensitive up-and down-bound with equal influences on …

Twin support vector machine (TSVM) is a novel machine learning algorithm, which aims at
finding two nonparallel planes for each class. In order to do so, one needs to resolve a pair …

Twin support vector machines (TWSVM) is a new machine learning method based on the
theory of Support Vector Machine (SVM). Unlike SVM, TWSVM would generate two non …

In general, pattern classification and regression tasks do not take into consideration the
variation in the importance of the training samples. For twin support vector regression …

Multi-class classification is an important problem in machine learning, which often occurs in
the real world and is an ongoing research issue. Support vector classification-regression …

Laplacian twin support vector machine (Lap-TSVM) is a state-of-the-art nonparallel-planes
semi-supervised classifier. It tries to exploit the geometrical information embedded in …