Primal least squares twin support vector regression

Huang, Huajuan; Ding, Shifei; Shi, Zhongzhi

doi:10.1631/jzus.ciip1301

Cited by 38 publications

(17 citation statements)

References 17 publications

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“…Denote ln AR   as the input sample matrix, where 12 ( , , , ) The central ideas in the nonparallel plane regressors, including TSVR [20] and LSTSVR [21], are to seek a pair of nonparallel functions in n -dimensional input space…”

Section: Tsvr and Its Least Square Version (Lstsvr)mentioning

confidence: 99%

“…For large-scale regression problem, TSVR, however, is not feasible, because of the need to solve two QPPs. To reduce the computational cost of TSVR and keep the advantages of TSVR, we have proposed a novel regressor, termed as Least Squares Twin Support Vector Regression (LSTSVR) [21]. A natural method for LSTSVR is to replace the inequality constraints in the above optimizations (2) and (3) …”

Section: Tsvr and Its Least Square Version (Lstsvr)mentioning

confidence: 99%

“…However, this solving method will be affected by time and memory constraints when dealing with the large datasets, which would make the learning speed of TSVR low. In order to improve the learning speed of TSVR, we have enhanced TSVR to least squares TSVR (LSTSVR) by introducing the least squares method [21]. For linear case, LSTSVR completely solves the regression problem with just two matrix inverses of much smaller dimensional matrix of order ( 1) …”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A sparse method for least squares twin support vector regression

Huang

Wei²,

Zhou

2016

Neurocomputing

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Section: Tsvr and Its Least Square Version (Lstsvr)mentioning

confidence: 99%

Section: Tsvr and Its Least Square Version (Lstsvr)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A sparse method for least squares twin support vector regression

Huang

Wei²,

Zhou

2016

Neurocomputing

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“…A primal version for TSVR (PTSVR) was presented in [99] by introducing the smooth function to approximate its loss function, PTSVR directly optimized the QPPs in the primal space based on a series of sets of linear equations. A least squares version for TSVR (PLSTSVR) was also considered in the primal space [100]. [101] introduced a simple and linearly convergent Lagrangian SVM algorithm for the dual of TSVR.…”

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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“…Support vector machine (SVM), introduced by Vapnik and co-workers, is an excellent kernel-based tool for solving classification and regression problems (Vapnik, 1995;Ding and Qi, 2012;Huang et al, 2012Huang et al, , 2013a. Unlike other machine learning methods, such as artificial neural network , the mathematical theory of SVM is based on the statistical learning theory, which has many advantages in solving small samples, non-linear and high dimensional pattern recognition problems.…”

Section: Introductionmentioning

confidence: 99%

Research on parameters optimisation of SVM based on swarm intelligence

Ding¹,

Huang²

2014

IJCI

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Support vector machine (SVM) is a new machine learning method based on statistical learning theory, which has become a hot research topic in the field of machine learning because of its excellent performance. However, the performance of SVM is very sensitive to its parameters. At present, swarm intelligence is the most common method to optimise the parameters of SVM. In this paper, the research on parameters optimisation of SVM based on swarm intelligence algorithms is reviewed. Firstly, we briefly introduce the theoretical basis of SVM. Secondly, we describe the latest progress of parameters optimisation of SVM based on swarm intelligence in recent years. Finally, we point out the research and development prospects of this kind of method.

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Primal least squares twin support vector regression

Cited by 38 publications

References 17 publications

A sparse method for least squares twin support vector regression

A sparse method for least squares twin support vector regression

Review on: Twin Support Vector Machines

Research on parameters optimisation of SVM based on swarm intelligence

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