The distribution of data has a significant impact on the results of classification. When the
distribution of one class is insignificant compared to the distribution of another class, data …

It has been shown that the structural information of data may contain useful prior domain
knowledge for training a classifier. How to apply the structural information of data to build a …

We improve the twin support vector machine (TWSVM) to be a novel nonparallel
hyperplanes classifier, termed as ITSVM (improved twin support vector machine), for binary …

Twin support vector machine (TSVM) has attracted significant attention in recent years, but it
is suitable for solving the single-task learning (STL) problems. It trains each task …

With the bloom of machine learning, pattern recognition plays an important role in many
aspects. However, traditional pattern recognition mainly focuses on single task learning …

Both support vector machine (SVM) and twin support vector machine (TWSVM) are powerful
classification tools. However, in contrast to many SVM-based feature selection methods …

Twin support vector machine (TSVM) is a novel machine learning algorithm, which aims at
finding two nonparallel planes for each class. In order to do so, one needs to resolve a pair …

Direct multi-task twin support vector machine (DMTSVM) is an effective algorithm to deal
with multi-task classification problems. However, the generated hyperplane may shift to …

Twin support vector machines are a recently proposed learning method for binary
classification. They learn two hyperplanes rather than one as in conventional support vector …

Twin K-class support vector classification (TKSVC) adopts 'One-vs.-One-vs.-Rest'structure to
utilise all the samples to increase the prediction accuracy. However, TKSVC is sensitive to …