Support vector machines (SVMs) are versatile learning models which are used for both
classification and regression. Several authors have reported successful applications of SVM …

Support vector machine (SVM) has attracted substantial interest in the community of
machine learning. As the extension of SVM, nonparallel hyperplane SVM (NHSVM) …

The enormous volumes of data created and maintained by industries, research institutions
are on the verge of outgrowing its infrastructure. The advancements in the organization's …

Breast cancer remains the most common type of cancer and the leading cause of cancer-
induced mortality among women with 2.4 million new cases diagnosed and 523,000 deaths …

The detection problem in statistical signal processing can be succinctly formulated: given m
(possibly) signal bearing, n-dimensional signal-plus-noise snapshot vectors (samples) and …

Proximal support vector machine (PSVM), as a variant of support vector machine (SVM), is to
generate a pair of non-parallel hyperplanes for classification. Although PSVM is one of the …

In twin support vector machines (TWSVMs), we determine pair of non-parallel planes by
solving two related SVM-type problems, each of which is smaller than the one in a …

A Twin Support Vector Machine (TWSVM), as a variant of a Multisurface Proximal Support
Vector Machine via Generalized Eigenvalues (GEPSVM), attempts to improve the …

In this paper, we propose a robust L1-norm non-parallel proximal support vector machine
(L1-NPSVM), which aims at giving a robust performance for binary classification in contrast …

The proximal support vector machine via generalized eigenvalues (GEPSVM) is a well-
known pattern classification method. GEPSVM, however, is prone to outliers due to its use of …