Vapnik VN. The nature of statistical learning theory. Berlin: Springer; 1995.

Book  Google Scholar 

Boser BE, Guyon IM, Vapnik VN. A training algorithm for optimal margin classifiers. ACM Press. 1992;10(1145/130385): 130401.

Google Scholar 

Zhao S, Liu S, Qiu B, Hong Z, Zhao D, Dong L. Leak detection method of liquid-filled pipeline based on vmd and svm. Urban Water J. 2023;20:1169–82. https://doi.org/10.1080/1573062X.2023.2251952.

Article  Google Scholar 

Kong L, Zhao X. An improved predictor for identifying recombination spots based on support vector machine. J Comp Methods in Sci and Eng. 2023;23:2485–96. https://doi.org/10.3233/JCM-226872.

Article  Google Scholar 

Surucu O, Gadsden SA, Yawney J. Condition monitoring using machine learning: A review of theory, applications, and recent advances. Expert Syst Appl. 2023;221: 119738. https://doi.org/10.1016/j.eswa.2023.119738.

Article  Google Scholar 

Hu L, Cui J. Digital image recognition based on fractional-order-pca-svm coupling algorithm. Measurement. 2019;145:150–9. https://doi.org/10.1016/j.measurement.2019.02.006.

Article  Google Scholar 

Liu Z, Zhang Q, Wang P, Li Z, Wang H. Automated classification of stems and leaves of potted plants based on point cloud data. Biosyst Eng. 2020;200:215–230. https://doi.org/10.48550/arXiv.2002.12536.

Jayadeva Khemchandani R, Chandra S. Twin support vector machines for pattern classification. IEEE Trans Pattern Anal Mach Intell. 2007;29:905–10. https://doi.org/10.1109/TPAMI.2007.1068.

Article  Google Scholar 

Peng X. Tpmsvm: A novel twin parametric-margin support vector machine for pattern recognition. Pattern Recogn. 2011;44:2678–92. https://doi.org/10.1016/j.patcog.2011.03.031.

Article  Google Scholar 

Huang X, Shi L, Suykens JAK. Support vector machine classifier with pinball loss. IEEE Trans Pattern Anal Mach Intell. 2014;36:984–97. https://doi.org/10.1109/TPAMI.2013.178.

Article  Google Scholar 

Yang L, Dong H. Support vector machine with truncated pinball loss and its application in pattern recognition. Chemom Intell Lab Syst. 2018;177:89–99. https://doi.org/10.1016/j.chemolab.2018.04.003.

Article  Google Scholar 

Huang X, Shi L, Suykens JAK. Ramp loss linear programming support vector machine. J Mach Learn Res. 2014;15:2185–211. https://doi.org/10.1016/j.procs.2016.05.432.

Article  MathSciNet  Google Scholar 

Shao YH, Zhang CH, Wang XB, Deng NY. Improvements on twin support vector machines. IEEE Trans Neural Netw. 2011;22:962–8. https://doi.org/10.1109/TNN.2011.2130540.

Article  Google Scholar 

Prasad SC, Anagha P, Balasundaram S. Robust pinball twin bounded support vector machine for data classification. Neural Process Lett. 2023; pp 1131–1153. https://doi.org/10.1007/s11063-022-10930-6.

Anagha P, Balasundaram S. On twin bounded support vector machine with pinball loss. In: Advanced Machine Intelligence and Signal Processing, Springer, 2022; pp 177–190

Zhang J, Lai Z, Kong H, Shen L. Robust twin bounded support vector classifier with manifold regularization. IEEE Transactions on Cybernetics, 2022; pp 1–16. https://doi.org/10.1109/TCYB.2022.3160013.

Xu Y, Yang Z, Pan X. A novel twin support-vector machine with pinball loss. IEEE Transactions on Neural Networks and Learning Systems. 2017;28:359–70. https://doi.org/10.1109/TNNLS.2015.2513006.

Article  MathSciNet  Google Scholar 

Zhang Q, Wang H, Yoon SW. A 1-norm regularized linear programming nonparallel hyperplane support vector machine for binary classification problems. Neurocomputing. 2020;376:141–52. https://doi.org/10.1016/j.neucom.2019.09.068.

Article  Google Scholar 

Tanveer M, Sharma A, Suganthan PN. General twin support vector machine with pinball loss function. Inf Sci. 2019;494:311–27. https://doi.org/10.1016/j.ins.2019.04.032.

Article  MathSciNet  Google Scholar 

Wang H, Shao Y. Fast generalized ramp loss support vector machine for pattern classification. Pattern Recogn. 2024;146: 109987. https://doi.org/10.1016/j.patcog.2023.109987.

Article  Google Scholar 

Liu M, Yang J, Li S, Zhou Z, Fan E, Zheng W. Robust gmm least square twin k-class support vector machine for urban water pipe leak recognition. Expert Syst Appl. 2022;195. https://doi.org/10.1016/j.eswa.2022.116525.

Wang H, Liu Y, Zhang S. Smooth and semi-smooth pinball twin support vector machine. Expert Syst Appl. 2023;226: 120189. https://doi.org/10.1016/j.eswa.2023.120189.

Article  Google Scholar 

Qi K, Yang H. Joint rescaled asymmetric least squared nonparallel support vector machine with a stochastic quasi-newton based algorithm. Appl Intell. 2022;52:14387–405. https://doi.org/10.1007/s10489-022-03183-2.

Article  Google Scholar 

Liu M, Shao Y, Li C, Chen W. Smooth pinball loss nonparallel support vector machine for robust classification. Appl Soft Comput. 2021;98: 106840. https://doi.org/10.1016/j.asoc.2020.106840.

Article  Google Scholar 

Wang H, Xu Y, Zhou Z. Twin-parametric margin support vector machine with truncated pinball loss. Neural Comput Appl. 2021;33:3781–98. https://doi.org/10.1007/s00521-020-05225-7.

Article  Google Scholar 

Wang H, Li G, Wang Z. Fast svm classifier for large-scale classification problems. Inf Sci. 2023;642: 119136. https://doi.org/10.1016/j.ins.2023.119136.

Article  Google Scholar 

Damminsed V, Panup W, Wangkeeree R. Laplacian twin support vector machine with pinball loss for semi-supervised classification. IEEE Access. 2023;11:31399–416. https://doi.org/10.1109/ACCESS.2023.3262270.

Article  Google Scholar 

Makmuang D, Ratiphaphongthon W, Wangkeeree R. Smooth support vector machine with generalized pinball loss for pattern classification. J Supercomput. 2023;79:11684–706. https://doi.org/10.1007/s11227-023-05082-w.

Article  Google Scholar 

Wang H, Lu S, Zhou Z. Ramp loss for twin multi-class support vector classification. Int J Syst Sci. 2020;51:1448–63. https://doi.org/10.1080/00207721.2020.1765047.

Article  MathSciNet  Google Scholar 

Pang X, Zhao J, Xu Y. A novel ramp loss-based multi-task twin support vector machine with multi-parameter safe acceleration. Neural Netw. 2022;150:194–212. https://doi.org/10.1016/j.neunet.2022.03.006.

Article  Google Scholar 

Tang L, Tian Y, Li W, Pardalos PM. Valley-loss regular simplex support vector machine for robust multiclass classification. Knowl-Based Syst. 2021;216. https://doi.org/10.1016/j.knosys.2021.106801.

Bartlett P, Jordan M, McAuliffe J. Convexity, classification, and risk bounds. J Am Stat Assoc. 2006;101:138–56. https://doi.org/10.1198/016214505000000907.

Article  MathSciNet  Google Scholar 

Yang Z, Xu Y. A safe accelerative approach for pinball support vector machine classifier. Knowl-Based Syst. 2018;147:12–24. https://doi.org/10.1016/j.knosys.2018.02.010.

Article  Google Scholar