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An improved parametric-margin universum TSVM
Motivated by the merit of twin support vector machine (TSVM), this paper presents an improved parametric-margin Universum twin support vector machine...
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An improved multi-task least squares twin support vector machine
In recent years, multi-task learning (MTL) has become a popular field in machine learning and has a key role in various domains. Sharing knowledge...
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\(\overline{\text {pin}}\)-TSVM: A Robust Transductive Support Vector Machine and its Application to the Detection of COVID-19 Infected Patients
Training a machine learning model on the data sets with missing labels is a challenging task. Not all models can handle the problem of missing...
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Safe sample screening for robust twin support vector machine
Twin support vector machine (TSVM) definitely improves computational speed compared with the classical SVM, and has been widely used in...
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Twin Bounded Support Vector Machine with Capped Pinball Loss
In order to obtain a more robust and sparse classifier, in this paper, we propose a novel classifier termed as twin bounded support vector machine...
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Support Vector Machine Based Models with Sparse Auto-encoder Based Features for Classification Problem
Auto-encoder is a special type of artificial neural network (ANN) that is used to learn informative features from data. In the literature, the...
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Sparse discriminant twin support vector machine for binary classification
For a binary classification problem, twin support vector machine (TSVM) has a faster learning speed than support vector machine (SVM) by seeking a...
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A unified kernel sparse representation framework for supervised learning problems
For supervised learning problems, a unified kernel sparse representation framework is proposed. It is applicable to almost all supervised learners in...
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A lagrangian-based approach for universum twin bounded support vector machine with its applications
The Universum provides prior knowledge about data in the mathematical problem to improve the generalization performance of the classifiers. Several...
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TSVMPath: Fast Regularization Parameter Tuning Algorithm for Twin Support Vector Machine
Twin support vector machine (TSVM) has attracted much attention in the field of machine learning with good generalization ability and computational...
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Federated Twin Support Vector Machine
TSVM is designed to solve binary classification problems with less computational overhead by finding two hyperplanes and has been widely used to...
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Deep Twin Support Vector Networks
Twin support vector machine (TSVM) is a successful improvement for traditional support vector machine (SVM) for binary classification. However, it is...
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Malware Detection Using Pseudo Semi-Supervised Learning
Malware, due to its ever-evolving nature, remains a serious threat. Sophisticated attacks using ransomware and viruses have crippled organizations...
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Sample Reduction Using \(\ell _1\) -Norm Twin Bounded Support Vector Machine
Twin support machine (TSVM) has a lower time complexity than support vector machine (SVM), but it has a poor ability to perform sample reduction. In...
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Twin-parametric margin support vector machine with truncated pinball loss
In this paper, we propose a novel classifier termed as twin-parametric margin support vector machine with truncated pinball loss (TPin-TSVM), which...
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Cucumber diseases diagnosis based on multi-class SVM and electronic medical record
Cucumber is one of the most popular vegetable varieties, but leaf disease of cucumber is the key factor restricting the increase of yield. Common...
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A novel method for solving universum twin bounded support vector machine in the primal space
In supervised learning, the Universum, a third class that is not a part of either class in the classification task, has proven to be useful. In this...
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A Novel Method for Solving Universum Twin Bounded Support Vector Machine in the Primal Space
In supervised learning, the Universum, a third class that is not a part of either class in the classification task, has proven to be useful. In this...
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Binary classification for imbalanced datasets using twin hyperspheres based on conformal method
Aiming at binary classification of highly imbalanced data, this paper proposes a novel twin-hypersphere method with conformal transformation. To...
