mlsanityData to 14 Oct 2026

Comprehensive Review On Twin Support Vector Machines

arXiv 2105.00336 ↗First posted 1 May 2021cs.LG cs.AIcited by 17

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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.

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

5 of 319 references.

References · 5

  1. 2021Ensemble deep learning: A reviewcited by 284
  2. 2017Insensitive Stochastic Gradient Twin Support Vector Machine for Large Scale Problemscited by 2
  3. 2017Scalable Twin Neural Networks for Classification of Unbalanced Datacited by 2
  4. 2018Ramp-based Twin Support Vector Clusteringcited by 1
  5. 2019A general model for plane-based clustering with loss functioncited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 3; 2024: 8; 2025: 1; 2026: 4.

17 papers.

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  1. 2023Detecting Clouds in Multispectral Satellite Images Using Quantum-Kernel Support Vector Machinescited by 15
  2. 2024Advancing Supervised Learning with the Wave Loss Function: A Robust and Smooth Approachcited by 11
  3. 2024Intuitionistic Fuzzy Generalized Eigenvalue Proximal Support Vector Machinecited by 11
  4. 2024Granular Ball Twin Support Vector Machinecited by 10
  5. 2024Multiview learning with twin parametric margin SVMcited by 8
  6. 2025TRKM: Twin Restricted Kernel Machines for Classification and Regressioncited by 8
  7. 2024Enhanced Feature Based Granular Ball Twin Support Vector Machinecited by 3
  8. 2022Diagnosis of Schizophrenia: A comprehensive evaluationcited by 2
  9. 2024GL-TSVM: A robust and smooth twin support vector machine with guardian loss functioncited by 2
  10. 2024Enhancing Robustness and Efficiency of Least Square Twin SVM via Granular Computingcited by 2
  11. 2023A Robust Twin Parametric Margin Support Vector Machine for Multiclass Classificationcited by 1
  12. 2026A Vision-and-Knowledge Enhanced Large Language Model for Generalizable Pedestrian Crossing Behavior Inferencecited by 1
5 more
  1. 2023Diagnosing Bipolar Disorder from 3-D Structural Magnetic Resonance Images Using a Hybrid GAN-CNN Methodcited by 0
  2. 2024Artificial Intelligence for Infectious Disease Prediction and Prevention: A Comprehensive Reviewcited by 0
  3. 2026Heaviside Low-Rank Support Matrix Machinecited by 0
  4. 2026Differential Spectral Damping Gap Adaptive Regularization for Ill-Conditioned Kernel Methodscited by 0
  5. 2026GBTSVR: Twin Support Vector Regression via Granular Ball Computingcited by 0