# Comprehensive Review On Twin Support Vector Machines

arXiv: https://arxiv.org/abs/2105.00336 · HTML: https://mlsanity.com/paper/2105.00336/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 319 references that are in the map.
- First posted on arXiv 1 May 2021.

## Builds on (most cited first, 5 of 5)

- [Ensemble deep learning: A review](https://mlsanity.com/paper/2104.02395/) (2021)
- [Insensitive Stochastic Gradient Twin Support Vector Machine for Large Scale Problems](https://mlsanity.com/paper/1704.05596/) (2017)
- [Scalable Twin Neural Networks for Classification of Unbalanced Data](https://mlsanity.com/paper/1705.00347/) (2017)
- [Ramp-based Twin Support Vector Clustering](https://mlsanity.com/paper/1812.03710/) (2018)
- [A general model for plane-based clustering with loss function](https://mlsanity.com/paper/1901.09178/) (2019)

## Built on by (most cited first, 17 of 17)

- [Detecting Clouds in Multispectral Satellite Images Using Quantum-Kernel Support Vector Machines](https://mlsanity.com/paper/2302.08270/) (2023)
- [Advancing Supervised Learning with the Wave Loss Function: A Robust and Smooth Approach](https://mlsanity.com/paper/2404.18101/) (2024)
- [Intuitionistic Fuzzy Generalized Eigenvalue Proximal Support Vector Machine](https://mlsanity.com/paper/2408.01713/) (2024)
- [Granular Ball Twin Support Vector Machine](https://mlsanity.com/paper/2410.04774/) (2024)
- [Multiview learning with twin parametric margin SVM](https://mlsanity.com/paper/2408.01981/) (2024)
- [TRKM: Twin Restricted Kernel Machines for Classification and Regression](https://mlsanity.com/paper/2502.15759/) (2025)
- [Enhanced Feature Based Granular Ball Twin Support Vector Machine](https://mlsanity.com/paper/2410.05786/) (2024)
- [Diagnosis of Schizophrenia: A comprehensive evaluation](https://mlsanity.com/paper/2203.11610/) (2022)
- [GL-TSVM: A robust and smooth twin support vector machine with guardian loss function](https://mlsanity.com/paper/2408.16336/) (2024)
- [Enhancing Robustness and Efficiency of Least Square Twin SVM via Granular Computing](https://mlsanity.com/paper/2410.17338/) (2024)
- [A Robust Twin Parametric Margin Support Vector Machine for Multiclass Classification](https://mlsanity.com/paper/2306.06213/) (2023)
- [A Vision-and-Knowledge Enhanced Large Language Model for Generalizable Pedestrian Crossing Behavior Inference](https://mlsanity.com/paper/2601.00694/) (2026)
- [Diagnosing Bipolar Disorder from 3-D Structural Magnetic Resonance Images Using a Hybrid GAN-CNN Method](https://mlsanity.com/paper/2310.07359/) (2023)
- [Artificial Intelligence for Infectious Disease Prediction and Prevention: A Comprehensive Review](https://mlsanity.com/paper/2411.10486/) (2024)
- [Heaviside Low-Rank Support Matrix Machine](https://mlsanity.com/paper/2603.00491/) (2026)
- [Differential Spectral Damping Gap Adaptive Regularization for Ill-Conditioned Kernel Methods](https://mlsanity.com/paper/2606.23407/) (2026)
- [GBTSVR: Twin Support Vector Regression via Granular Ball Computing](https://mlsanity.com/paper/2610.20053/) (2026)

## Abstract (arXiv)

Twin support vector machine (TWSVM) and twin support vector regression (TSVR) are newly emerging efficient machine learning techniques which offer promising solutions for classification and regression challenges respectively. TWSVM is based upon the idea to identify two nonparallel hyperplanes which classify the data points to their respective classes. It requires to solve two small sized quadratic programming problems (QPPs) in lieu of solving single large size QPP in support vector machine (SVM) while TSVR is formulated on the lines of TWSVM and requires to solve two SVM kind problems. Although there has been good research progress on these techniques; there is limited literature on the comparison of different variants of TSVR. Thus, this review presents a rigorous analysis of recent research in TWSVM and TSVR simultaneously mentioning their limitations and advantages. To begin with we first introduce the basic theory of support vector machine, TWSVM and then focus on the various improvements and applications of TWSVM, and then we introduce TSVR and its various enhancements. Finally, we suggest future research and development prospects.