A weighted twin support vector regression

Xu, Yitian; Wang, Lai-Sheng

doi:10.1016/j.knosys.2012.03.013

Cited by 102 publications

(31 citation statements)

References 19 publications

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“…[93] improved the TSVR by formulating it as a pair of linear programming problems instead of QPPs. Weighted TSVR [94] assigned the samples in the different positions with different penalties, which can avoid the over-fitting problem to a certain extent. ε-TSVR [95] implemented the structural risk minimization principle by introducing the regularization term in primal problems of TSVR, and the SOR technique was used to solve the optimization problems to speed up the training procedure.…”

Section: Variants Of Twin Support Vector Regressions (Twsvrs)mentioning

confidence: 99%

Review on: Twin Support Vector Machines

Tian

2014

Ann. Data. Sci.

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Twin support vector machine (TWSVM), an useful extension of the traditional SVM, becomes the current researching hot spot in machine learning during the last few years. For the binary classification problem, the basic idea of TWSVM is to seek two nonparallel proximal hyperplanes such that each hyperplane is closer to one of the two classes and is at least one distance from the other. TWSVM has lower computational complexity and better generalization ability, therefore in the last few years it has been studied extensively and developed rapidly. Considering the many variants of TWSVM, a systematic survey is needed and helpful to understand and use this family of data mining techniques more easily. The purpose of this paper is to closely review TWSVMs and provide an insightful understanding of current developments, at the same time point out their limitations and highlight the major opportunities and challenges, as well as potential important research directions.

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Section: Variants Of Twin Support Vector Regressions (Twsvrs)mentioning

confidence: 99%

Review on: Twin Support Vector Machines

Tian

2014

Ann. Data. Sci.

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“…A weighted twin version of -SVR can be found in [32]. In this feature space we should look for the linear function…”

Section: -Support Vector Regression Estimatormentioning

confidence: 99%

Nonparametric bivariate copula estimation based on shape-restricted support vector regression

Wang

2012

Knowledge-Based Systems

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“…The strategy of solving two smaller-sized QPPs, rather than a single larger-sized QPP, makes the learning speed of the TSVM faster than that of the standard SVM in theory. Many its variants have been proposed, such as ν-TSVM [8], rough ν-TSVM [9], least square TSVM [10,11], twin support vector regression [12,13], twin parametric-margin SVM (TPMSVM) [14,15] and so on.…”

Section: Introductionmentioning

confidence: 99%

A weighted twin support vector regression

Cited by 102 publications

References 19 publications

Review on: Twin Support Vector Machines

Review on: Twin Support Vector Machines

Nonparametric bivariate copula estimation based on shape-restricted support vector regression

Laplacian twin parametric-margin support vector machine for semi-supervised classification

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