Probabilistic outputs for twin support vector machines
Knowledge-Based Systems
Robust twin support vector machine for pattern classification
Pattern Recognition
Accurate Prediction of Coronary Artery Disease Using Reliable Diagnosis System
Journal of Medical Systems
A regularization for the projection twin support vector machine
Knowledge-Based Systems
Twin support vector machine with Universum data
Neural Networks
Structural twin support vector machine for classification
Knowledge-Based Systems
Ensemble learning for generalised eigenvalues proximal support vector machines
International Journal of Computer Applications in Technology
A proximal classifier with consistency
Knowledge-Based Systems
Twin least squares support vector regression
Neurocomputing
Large-scale linear nonparallel support vector machine solver
Neural Networks
Nonparallel hyperplane support vector machine for binary classification problems
Information Sciences: an International Journal
A nonparallel support vector machine for a classification problem with universum learning
Journal of Computational and Applied Mathematics
Least squares twin parametric-margin support vector machine for classification
Applied Intelligence
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For classification problems, the generalized eigenvalue proximal support vector machine (GEPSVM) and twin support vector machine (TWSVM) are regarded as milestones in the development of the powerful SVMs, as they use the nonparallel hyperplane classifiers. In this brief, we propose an improved version, named twin bounded support vector machines (TBSVM), based on TWSVM. The significant advantage of our TBSVM over TWSVM is that the structural risk minimization principle is implemented by introducing the regularization term. This embodies the marrow of statistical learning theory, so this modification can improve the performance of classification. In addition, the successive overrelaxation technique is used to solve the optimization problems to speed up the training procedure. Experimental results show the effectiveness of our method in both computation time and classification accuracy, and therefore confirm the above conclusion further.