Among the extensions of twin support vector machine (TSVM), some scholars have utilized
K-nearest neighbor (KNN) graph to enhance TSVM's classification accuracy. However …

Twin-KSVC, as a novel multi-class classification algorithm, aims at finding two nonparallel
hyper-planes for the two focused classes of samples by solving a pair of smaller-sized …

K-nearest neighbor based structural twin support vector machine (KNN-STSVM) performs
better than structural twin support vector machine (S-TSVM). It applies the intra-class KNN …

A twin support vector machine (TWSVM) is an effective classifier, especially for binary data,
which is defined by squared l 2-norm distance in the objective function. Since squared l 2 …

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 …

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 …

The standard twin support vector machine (TSVM) uses the hinge loss function which leads
to noise sensitivity and instability. In this paper, we propose a novel general twin support …

In twin support vector machines (TSVM), noise blurs the boundary between positive and
negative samples, increasing the probability of classification errors. In this article, we …

Least squares twin support vector machine (LSTSVM) is an effective and efficient learning
algorithm for pattern classification. However, the distance in LSTSVM is measured by …

Twin support vector machines (TWSVM) with hinge loss suffer from noise sensitivity and
instability. To overcome these issues, pinball loss based general twin support vector …