Context Ensemble methods consist of combining more than one single technique to solve
the same task. This approach was designed to overcome the weaknesses of single …

Ensemble methods try to improve performance via integrating different kinds of input data,
features, or learning algorithms. In addition to other areas, they are finding their applications …

Abstract Twin Support Vector Machine (TWSVM) is an emerging machine learning method
suitable for both classification and regression problems. It utilizes the concept of …

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 …

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 …

Finding abnormalities in diagnostic images is a difficult task even for expert radiologists
because the normal tissue locations largely outnumber those with suspicious signs which …

Universum, as third class that does not belong to the positive class and negative class,
allows to incorporate the prior knowledge into the learning process. A lot of reaserchers …

Background Today, there are a lot of markers on the prognosis and diagnosis of complex
diseases such as primary breast cancer. However, our understanding of the drivers that …

How to select the suitable parameters and kernel model is a very important problem for Twin
Support Vector Machines (TSVMs). In order to solve this problem, one solving algorithm …

Recently, both Deep Cascade classifiers and Convolutional Neural Networks (CNNs) have
achieved state-ofthe-art microcalcification (MC) detection performance in digital …