A sparse method for least squares twin support vector regression

Huang, Huajuan; Wei, Xiuxi; Zhou, Yongquan

doi:10.1016/j.neucom.2015.12.133

Cited by 16 publications

(1 citation statement)

References 21 publications

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“…In 2014, Chen et al [25] introduced the regularization into the formulation of TSVR and implemented L1-norm loss function to make it robust and sparse simultaneously. Huang et al [58] formulated a sparse version of least square TSVR by introducing a regularization term to make it strongly convex and also converted the primal problems to linear programming problems. This leads to a sparse solution with significantly less computational time.…”

Section: Robust and Sparse Twin Support Vector Regressionmentioning

confidence: 99%

Comprehensive Review On Twin Support Vector Machines

Tanveer,

Rajani,

Rastogi

et al. 2021

Preprint

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Twin support vector machine (TSVM) and twin support vector regression (TSVR) are newly emerging efficient machine learning techniques which offer promising solutions for classification and regression challenges respectively. TSVM 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 TSVM 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 TSVM and TSVR simultaneously mentioning their limitations and advantages. To begin with we first introduce the basic theory of TSVM and then focus on the various improvements and applications of TSVM, and then we introduce TSVR and its various enhancements. Finally, we suggest future research and development prospects.

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Section: Robust and Sparse Twin Support Vector Regressionmentioning

confidence: 99%