Twin least squares support vector regression

Zhao, Yong-Ping; Jing, Zhao; Zhao, Min

doi:10.1016/j.neucom.2013.03.005

Cited by 50 publications

(21 citation statements)

References 39 publications

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“…The one interesting work is to introduce the idea in the twin parametric insensitive support vector regression (TPISVR) [27], which can derive a sparse regressor. Another further work is to extend this ITSVR algorithm to other TSVR tools [28][29][30] In addition, it is worth to analyzing the relation between the performance and parameters of ITSVR. …”

Section: Discussionmentioning

confidence: 99%

Interval twin support vector regression algorithm for interval input-output data

Peng

Chen

Kong

et al. 2015

Int. J. Mach. Learn. & Cyber.

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It is necessary to use interval data to define terms or describe extreme behaviors because of the existence of uncertainty in many real-world problems. In this paper, a novel efficient interval twin support vector regression (ITSVR) is proposed to handle such interval data. This ITSVR employs two nonparallel functions to identify the upper and lower sides of the interval output data, respectively, in which the Hausdorff distance is incorporated into the Gaussian kernel as the interval kernel for interval input data. Compared with other support vector regression (SVR)-based interval regression methods, such as the interval support vector interval regression networks (ISVIRN), this ITSVR algorithm is more efficient since only two smaller-sized QPPs are solved, respectively. The experimental results on several artificial datasets and three stock index datasets show the validity of ITSVR.

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Section: Discussionmentioning

confidence: 99%

Interval twin support vector regression algorithm for interval input-output data

Peng

Chen

Kong

et al. 2015

Int. J. Mach. Learn. & Cyber.

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“…This section briefly recalls single-output TLS-RSVR and singleoutput TSVR, -ε for details, see [7,8]. Given a single-output data set…”

Section: Preliminariesmentioning

confidence: 99%

“…We all know that twin least squares regularized SVR (TLS-RSVR) [7] and twin -ε SVR ( ) TSVR -ε [8] are two commonly used single-output regressors and are all an extension and improvement of TSVR presented by Peng [9]. Different from only empirical risk minimization being implemented in TSVR, the structural risk minimization is also ( ),…”

Section: Introductionmentioning

confidence: 99%

FAST M-TLS-RSVR AND M-LS-e-TSVR FOR MULTI-OUTPUT REGRESSION

Zhao¹,

Fan²

2017

Int. J. Appl. Math. Mach. Learning

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This paper focuses on research multi-output regression problems and proposes two novel fast learning algorithms named as fast multi-output twin least squares regularized SVR (FM-TLS-RSVR) and fast multi-output least squaresrespectively. The main advantage of the proposed methods is to consider the cross relations among output vectors as a whole and avoid the singularity of matrices. In addition, the proposed FM-TLS-RSVR also possesses the sparsity. Experiment results indicate that FM-TLS-RSVR and TSVR --LS -FM ε are two effective and competitive multi-output regressors.

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“…Its lease squares version was developed in [92]. [93] improved the TSVR by formulating it as a pair of linear programming problems instead of QPPs.…”

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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Twin least squares support vector regression

Cited by 50 publications

References 39 publications

Interval twin support vector regression algorithm for interval input-output data

Interval twin support vector regression algorithm for interval input-output data

FAST M-TLS-RSVR AND M-LS-e-TSVR FOR MULTI-OUTPUT REGRESSION

Review on: Twin Support Vector Machines

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