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Twin Bounded Least Squares Support Vector Regression
Support Vector Machine (SVM) has received much attention in machine learning due to its profound theoretical research and practical application...
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Projection generalized correntropy twin support vector regression
A projection generalized maximum correntropy twin support vector regression algorithm is proposed. The generalized correntropy function is added into...
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Foretelling the compressive strength of concrete using twin support vector regression
Characteristic compressive strength is a key and crucial physical attribute of concrete used in various design standards and rules. In this study,...
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Robust twin support vector regression with correntropy-based metric
Machine learning methods have been widely used control and information systems. Robust learning is an important issue in machine learning field. In...
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Twin Support Vector Regression with Privileged Information
In this paper, we propose a novel framework called Twin Support Vector Regression with Privileged Information (TSVR+), which aims to improve the...
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A novel fuzzy twin support vector machine using mass-based dissimilarity measure
To mitigate the negative impact of noise on twin support vector machines (TWSVM), researchers have integrated fuzzy set theory with TWSVM, utilizing...
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Robust Twin Support Vector Regression with Smooth Truncated Hε Loss Function
Twin support vector regression (TSVR) is an important algorithm to handle regression problems developed on the basis of support vector regression...
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GBTWSVM: Granular-Ball Twin Support Vector Machine
Twin Support Vector Machine (TWSVM) has gained popularity as a machine learning tool due to its low computational complexity. However, it may not be...
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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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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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Online Learning Approach Based on Recursive Formulation for Twin Support Vector Machine and Sparse Pinball Twin Support Vector Machine
In this paper, an online approach was proposed for twin support vector machine motivated by online learning algorithms for double-weighted least...
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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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A bilateral assessment of human activity recognition using grid search based nonlinear multi-task least squares twin support vector machine
The recognition of individual activity has proven its importance in many application areas. Even after the pandemic crisis worldwide, the remote...
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Functional iterative approach for Universum-based primal twin bounded support vector machine to EEG classification (FUPTBSVM)
Due to the increasing popularity of support vector machine (SVM) and the introduction of Universum, many variants of SVM along with Universum such as...
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Least squares structural twin bounded support vector machine on class scatter
Several projects and application development teams are spending their precious time and energy in the field of classification and regression. So, the...
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A Bilateral Assessment of Human Activities Using PSO-Based Feature Optimization and Non-linear Multi-task Least Squares Twin Support Vector Machine
Human activity recognition (HAR) is an essential part of many applications, including smart surroundings, sports analysis, and healthcare. Accurately...
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EEG signal classification using improved intuitionistic fuzzy twin support vector machines
Support-vector machines (SVMs) have been successfully employed to diagnose neurological disorders like epilepsy and sleep disorders via...
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EEG Signal Classification Using a Novel Universum-Based Twin Parametric-Margin Support Vector Machine
The Universum data, which indicates a sample that does not belong to any of the classes, has been proved to be useful in supervised learning. The...
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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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Multi-task twin spheres support vector machine with maximum margin for imbalanced data classification
Multi-task learning (MTL) has been gradually developed to be a quite effective method recently. Different from the single-task learning (STL), MTL...
