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

Abstract Support Vector Machine (SVM) and its variants are gaining momentum among the
Machine Learning community. In this paper, we present a quantitative analysis between the …

In this paper, we propose a novel classifier termed as twin-parametric margin support vector
machine with truncated pinball loss (TPin-TSVM), which is motivated by the twin-parametric …

The least squares twin support vector machine (LSTSVM) generates two non-parallel
hyperplanes by directly solving a pair of linear equations as opposed to solving two …

Universum-based support vector machine incorporates prior information about the
distribution of data in training of the classifier. This leads to better generalization …

Traditional hinge loss function-based large-scale support vector machine (SVM) algorithms
tend to perform poorly in the presence of noise, especially when the model is trained …

Abstract Twin Support Vector Machines (TWSVMs) have emerged as an efficient alternative
to Support Vector Machines (SVM) for learning from imbalanced datasets. The TWSVM …

Least squares twin support vector machine (LSTSVM) is a new machine learning method, as
opposed to solving two quadratic programming problems in twin support vector machine …

In this paper, we propose a novel maximal margin hyper-sphere support vector machine
(MMHS-SVM) for binary pattern classification. Our proposed MMHS-SVM aims to find two …

Random vector functional link (RVFL) has always proven to be an excellent classifier in
various application areas of machine learning. In this work, inspired by RVFL and its twin …