Twin support vector machine (TWSVM) is a classical and effective classifier for binary
classification. However, its robustness cannot be guaranteed due to the utilization of …

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 …

In the new paradigm, learning using privileged information (LUPI) creates a more
informative strategy for tasks to achieve better prediction. SVM based methods including …

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 …

To improve the generalization performance, we develop a new technique for handling the
impacts of outliers using Lagrangian twin bounded SVM (TBSVM) with kernel fuzzy …

Off-the-shelf dictionary learning algorithms have achieved satisfactory results in small
sample face recognition applications. However, the achieved results depend on the facial …

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

A novel binary classification approach is proposed in this paper, extending the ideas behind
nonparallel support vector machine (NPSVM) to robust machine learning. NPSVM …

A twin bounded support vector machine (TBSVM) is a phenomenon of symmetry that
improves the performance of the traditional support vector machine classification algorithm …

Proximal support vector machine via generalized eigenvalues (GEPSVM) is one of the most
successful methods for classification problems. However, GEPSVM is vulnerable to outliers …