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

At present, the excellent performance of support vector machine (SVM) has made it
successfully applied in many fields. However, when SVM is used for two-dimensional matrix …

Twin support vector machine (TWSVM) has gained increasing interest from various research
fields recently. In this paper, we aim to report the current state of the theoretical research and …

We propose a novel nonparallel classifier, called nonparallel support vector machine
(NPSVM), for binary classification. Our NPSVM that is fully different from the existing …

In this paper, we propose an efficient weighted Lagrangian twin support vector machine
(WLTSVM) for the imbalanced data classification based on using different training points for …

Support vector machine (SVM), as a useful tool of classification, has been widely applied in
many fields. However, it may incur computationally infeasibility on very large sample …

The purpose of this research is to introduce an innovative decision architecture to assess
corporate risks by utilizing accounting narratives and to further examine the association …

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 …

Twin support vector machines (TWSVM), a novel machine learning algorithm developing
from traditional support vector machines (SVM), is one of the typical nonparallel support …

It is an important component of risk management in financial markets to develop an early
warning systems (EWS) for extreme financial risk. In this paper, we establish a novel EWS …